CDC Daily by State
davegoblue
2023-04-10
Background
This file is designed to use CDC data to assess coronavirus disease burden by state, including creating and analyzing state-level clusters.
Through March 7, 2021, The COVID Tracking Project collected and integrated data on tests, cases, hospitalizations, deaths, and the like by state and date. The latest code for using this data is available in Coronavirus_Statistics_CTP_v004.Rmd.
The COVID Tracking Project suggest that US federal data sources are now sufficiently robust to be used for analyses that previously relied on COVID Tracking Project.
The code in this module builds on code available in _v005, with function and mapping files updated:
- Generic_Added_Utility_Functions_202105_v001.R - generic functions
that can be used in other areas
- Coronavirus_CDC_Daily_Functions_v002.R - functions specific to coronavirus daily data
Broadly, the CDC data analyzed by this module includes:
- CDC weekly case and death data by state and date are available for
download on the CDC
website
- CDC hospital data are available for download on the healthdata.gov
website
- CDC vaccines data are also available for download on the CDC website:
Functions and Mapping Files
The tidyverse package is loaded and functions are sourced:
# The tidyverse functions are routinely used without package::function format
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.0 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.1 ✔ tibble 3.1.8
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the ]8;;http://conflicted.r-lib.org/conflicted package]8;; to force all conflicts to become errorslibrary(geofacet)
# Functions are available in source file
source("./Generic_Added_Utility_Functions_202105_v001.R")
source("./Coronavirus_CDC_Daily_Functions_v002.R")A series of mapping files are also available to allow for parameterized processing. Mappings include:
- urlMapper - mapping file for urlType and url location to download
data
- renMapper - mapping file for renaming of variables in the raw data
file
- selfListMapper - mapping file for transformations by variable
type
- fullListMapper - mapping file for transformations across variable
types
- lstComboMapper - mapping file for elements to be combined by data
type (most common is to combine NYC and NYS data to NY if the file
provides them separately)
- uqMapper - mapping file for fields that should combine to be unique
keys for processed files
- lstFilterMapper - mapping file for filtering to subset (most common
is to keep 50 states and DC)
- vecSelectMapper - mapping file for variables to keep
- checkControlGroupMapper - mapping file for group_by() of control
total checks
- checkControlAggMapper - mapping file for numeric variables for
control total checks
- checkSimilarityMapper - mapping file for similarity checks to
perform
- plotSimilarityMaooer - mapping file for fields where differences in universe should be plotted
- keyAggMapper - mapping file for the aggregate-level control total
checks to perform
- perCapMapper - named vector that drives conversion from original
field name to per capita field name
- hhsMapper - named vector that drivers numerical variables to keep (and renaming) from HHS capacity data file
These default parameters are maintained in a separate .R file and can be sourced:
# These have been updated to _v003
source("./Coronavirus_CDC_Daily_Default_Mappings_v003.R")Example Code Processing
The function is run on previously downloaded data:
The latest data are downloaded and processed:
readList <- list("cdcWeeklyBurden"="./RInputFiles/Coronavirus/CDC_dc_wkly_downloaded_230402.csv",
"cdcHosp"="./RInputFiles/Coronavirus/CDC_h_downloaded_230402.csv",
"vax"="./RInputFiles/Coronavirus/vaxData_downloaded_230402.csv"
)
compareList <- list("cdcWeeklyBurden"=readFromRDS("cdc_daily_230302")$dfRaw$cdcWeeklyBurden,
"cdcHosp"=readFromRDS("cdc_daily_230302")$dfRaw$cdcHosp,
"vax"=readFromRDS("cdc_daily_230302")$dfRaw$vax
)
cdc_daily_230402 <- readRunCDCDaily(thruLabel="Mar 31, 2023",
downloadTo=lapply(readList, FUN=function(x) if(file.exists(x)) NA else x),
readFrom=readList,
compareFile=compareList,
writeLog=NULL,
useClusters=readFromRDS("cdc_daily_210528")$useClusters,
weightedMeanAggs=c("tcpm7", "tdpm7", "cpm7", "dpm7", "hpm7",
"vxcpm7", "vxcgte65pct"
),
skipAssessmentPlots=FALSE,
brewPalette="Paired"
)##
## No file has been downloaded, will use existing file: ./RInputFiles/Coronavirus/CDC_dc_wkly_downloaded_230402.csv## Rows: 10020 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): date_updated, state, start_date, end_date
## dbl (6): tot_cases, new_cases, tot_deaths, new_deaths, new_historic_cases, n...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.##
## *** File has been checked for uniqueness by: state date## Warning: There was 1 warning in `summarize()`.
## ℹ In argument: `across(.cols = all_of(useVars), .fns = fn, ...)`.
## ℹ In group 1: `date = 2020-01-22`.
## Caused by warning:
## ! The `...` argument of `across()` is deprecated as of dplyr 1.1.0.
## Supply arguments directly to `.fns` through an anonymous function instead.
##
## # Previously
## across(a:b, mean, na.rm = TRUE)
##
## # Now
## across(a:b, \(x) mean(x, na.rm = TRUE))## Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
## ℹ Please use tidy evaluation ideoms with `aes()`
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 4
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2021-12-08 new_deaths 6465 8857 2392 0.31223078
## 2 2023-01-11 new_deaths 4108 4448 340 0.07947639
## 3 2022-06-01 new_deaths 1994 2103 109 0.05320967
## 4 2021-07-14 new_cases 251653 178256 73397 0.34145366
## 5 2021-07-07 new_cases 134473 109537 24936 0.20438507
## 6 2023-01-18 new_cases 338956 316627 22329 0.06811952
## 7 2021-06-02 new_cases 120788 113131 7657 0.06546711
## 8 2020-11-04 new_cases 652612 694701 42089 0.06247843
## 9 2020-08-12 new_cases 399531 377082 22449 0.05781258
## 10 2021-08-11 new_cases 851192 901672 50480 0.05759717
## 11 2020-08-19 new_cases 349383 330558 18825 0.05537245
## 12 2023-01-11 new_cases 432643 455887 23244 0.05232012
## 13 2021-06-30 new_cases 98741 93823 4918 0.05107912## Warning in left_join(., df, by = names(univData)): Each row in `x` is expected to match at most 1 row in `y`.
## ℹ Row 1 of `x` matches multiple rows.
## ℹ If multiple matches are expected, set `multiple = "all"` to silence this
## warning.## Warning in left_join(., ref, by = names(univData)): Each row in `x` is expected to match at most 1 row in `y`.
## ℹ Row 1 of `x` matches multiple rows.
## ℹ If multiple matches are expected, set `multiple = "all"` to silence this
## warning.
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 MO tot_deaths 2041320 1889376 151944 0.077311499
## 2 MN tot_deaths 1323494 1311962 11532 0.008751427
## 3 RI tot_deaths 398207 399829 1622 0.004064980
## 4 MO tot_cases 134077761 135468146 1390385 0.010316499
## 5 FL tot_cases 556254722 561378228 5123506 0.009168495
## 6 DE tot_cases 24269412 24152458 116954 0.004830627
## 7 TX tot_cases 642477094 640839111 1637983 0.002552735
## 8 GU new_deaths 415 420 5 0.011976048
## 9 MO new_deaths 20214 20354 140 0.006901992
## 10 VT new_deaths 930 925 5 0.005390836
## 11 AR new_deaths 12930 12980 50 0.003859514
## 12 MN new_deaths 14448 14402 46 0.003188908
## 13 RI new_deaths 3870 3860 10 0.002587322
## 14 DE new_deaths 3329 3324 5 0.001503081
## 15 OK new_deaths 15411 15432 21 0.001361735
## 16 MO new_cases 1764510 1773152 8642 0.004885713
## 17 FL new_cases 7499410 7528420 29010 0.003860837
## 18 DE new_cases 331763 330817 946 0.002855504
## 19 KY new_cases 1716242 1713684 2558 0.001491577
## 20 OK new_cases 1282596 1284450 1854 0.001444462
## 21 MP new_cases 13680 13666 14 0.001023916
##
##
##
## Raw file for cdcWeeklyBurden:
## Rows: 10,020
## Columns: 10
## $ date_updated <chr> "01/23/2020", "01/30/2020", "02/06/2020", "02/13/2…
## $ state <chr> "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", "A…
## $ start_date <chr> "01/16/2020", "01/23/2020", "01/30/2020", "02/06/2…
## $ date <date> 2020-01-22, 2020-01-29, 2020-02-05, 2020-02-12, 2…
## $ tot_cases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 11, 63, 149, 235, 300, 337…
## $ new_cases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 11, 52, 86, 86, 65, 37, 18…
## $ tot_deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 7, 9, 9, 9, 10, 1…
## $ new_deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 4, 2, 0, 0, 1, 0,…
## $ new_historic_cases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ new_historic_deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
##
## No file has been downloaded, will use existing file: ./RInputFiles/Coronavirus/CDC_h_downloaded_230402.csv## Rows: 60599 Columns: 135
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): state
## dbl (132): critical_staffing_shortage_today_yes, critical_staffing_shortage...
## lgl (1): geocoded_state
## date (1): date
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 31
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2023-03-02 hosp_ped 1143 1038 105 0.09628611## Warning in left_join(., df, by = names(univData)): Each row in `x` is expected to match at most 1 row in `y`.
## Each row in `x` is expected to match at most 1 row in `y`.
## ℹ Row 1 of `x` matches multiple rows.
## ℹ If multiple matches are expected, set `multiple = "all"` to silence this
## warning.
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 VI inp 6233 6118 115 0.018621974
## 2 AK inp 71283 71837 554 0.007741755
## 3 KS inp 423272 422782 490 0.001158318
## 4 VI hosp_ped 158 147 11 0.072131148
## 5 AK hosp_ped 3229 3258 29 0.008940959
## 6 KS hosp_ped 5987 5969 18 0.003011040
## 7 PR hosp_ped 27722 27684 38 0.001371693
## 8 NH hosp_ped 1572 1570 2 0.001273074
## 9 IA hosp_ped 9618 9606 12 0.001248439
## 10 HI hosp_ped 4559 4554 5 0.001097333
## 11 WY hosp_ped 987 988 1 0.001012658
## 12 VI hosp_adult 5910 5806 104 0.017753499
## 13 AK hosp_adult 65959 66484 525 0.007927939
## 14 KS hosp_adult 393875 393403 472 0.001199068
## 15 PR hosp_adult 251141 250889 252 0.001003924
##
##
##
## Raw file for cdcHosp:
## Rows: 60,599
## Columns: 135
## $ state <chr> …
## $ date <date> …
## $ critical_staffing_shortage_today_yes <dbl> …
## $ critical_staffing_shortage_today_no <dbl> …
## $ critical_staffing_shortage_today_not_reported <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_yes <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_no <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_not_reported <dbl> …
## $ hospital_onset_covid <dbl> …
## $ hospital_onset_covid_coverage <dbl> …
## $ inpatient_beds <dbl> …
## $ inpatient_beds_coverage <dbl> …
## $ inpatient_beds_used <dbl> …
## $ inpatient_beds_used_coverage <dbl> …
## $ inp <dbl> …
## $ inpatient_beds_used_covid_coverage <dbl> …
## $ previous_day_admission_adult_covid_confirmed <dbl> …
## $ previous_day_admission_adult_covid_confirmed_coverage <dbl> …
## $ previous_day_admission_adult_covid_suspected <dbl> …
## $ previous_day_admission_adult_covid_suspected_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_suspected <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_coverage <dbl> …
## $ staffed_adult_icu_bed_occupancy <dbl> …
## $ staffed_adult_icu_bed_occupancy_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_coverage <dbl> …
## $ hosp_adult <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_coverage <dbl> …
## $ hosp_ped <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_coverage <dbl> …
## $ total_staffed_adult_icu_beds <dbl> …
## $ total_staffed_adult_icu_beds_coverage <dbl> …
## $ inpatient_beds_utilization <dbl> …
## $ inpatient_beds_utilization_coverage <dbl> …
## $ inpatient_beds_utilization_numerator <dbl> …
## $ inpatient_beds_utilization_denominator <dbl> …
## $ percent_of_inpatients_with_covid <dbl> …
## $ percent_of_inpatients_with_covid_coverage <dbl> …
## $ percent_of_inpatients_with_covid_numerator <dbl> …
## $ percent_of_inpatients_with_covid_denominator <dbl> …
## $ inpatient_bed_covid_utilization <dbl> …
## $ inpatient_bed_covid_utilization_coverage <dbl> …
## $ inpatient_bed_covid_utilization_numerator <dbl> …
## $ inpatient_bed_covid_utilization_denominator <dbl> …
## $ adult_icu_bed_covid_utilization <dbl> …
## $ adult_icu_bed_covid_utilization_coverage <dbl> …
## $ adult_icu_bed_covid_utilization_numerator <dbl> …
## $ adult_icu_bed_covid_utilization_denominator <dbl> …
## $ adult_icu_bed_utilization <dbl> …
## $ adult_icu_bed_utilization_coverage <dbl> …
## $ adult_icu_bed_utilization_numerator <dbl> …
## $ adult_icu_bed_utilization_denominator <dbl> …
## $ geocoded_state <lgl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+_coverage` <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown_coverage <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+_coverage` <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown_coverage <dbl> …
## $ deaths_covid <dbl> …
## $ deaths_covid_coverage <dbl> …
## $ on_hand_supply_therapeutic_a_casirivimab_imdevimab_courses <dbl> …
## $ on_hand_supply_therapeutic_b_bamlanivimab_courses <dbl> …
## $ on_hand_supply_therapeutic_c_bamlanivimab_etesevimab_courses <dbl> …
## $ previous_week_therapeutic_a_casirivimab_imdevimab_courses_used <dbl> …
## $ previous_week_therapeutic_b_bamlanivimab_courses_used <dbl> …
## $ previous_week_therapeutic_c_bamlanivimab_etesevimab_courses_used <dbl> …
## $ icu_patients_confirmed_influenza <dbl> …
## $ icu_patients_confirmed_influenza_coverage <dbl> …
## $ previous_day_admission_influenza_confirmed <dbl> …
## $ previous_day_admission_influenza_confirmed_coverage <dbl> …
## $ previous_day_deaths_covid_and_influenza <dbl> …
## $ previous_day_deaths_covid_and_influenza_coverage <dbl> …
## $ previous_day_deaths_influenza <dbl> …
## $ previous_day_deaths_influenza_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_coverage <dbl> …
## $ all_pediatric_inpatient_bed_occupied <dbl> …
## $ all_pediatric_inpatient_bed_occupied_coverage <dbl> …
## $ all_pediatric_inpatient_beds <dbl> …
## $ all_pediatric_inpatient_beds_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4 <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17 <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11 <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown_coverage <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_coverage <dbl> …
## $ staffed_pediatric_icu_bed_occupancy <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_coverage <dbl> …
## $ total_staffed_pediatric_icu_beds <dbl> …
## $ total_staffed_pediatric_icu_beds_coverage <dbl> …
##
## No file has been downloaded, will use existing file: ./RInputFiles/Coronavirus/vaxData_downloaded_230402.csv## Rows: 38104 Columns: 109
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Date, Location
## dbl (107): MMWR_week, Distributed, Distributed_Janssen, Distributed_Moderna,...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 5
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 1 and at least 1%
##
## [1] date name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)## Warning in left_join(., df, by = names(univData)): Each row in `x` is expected to match at most 1 row in `y`.
## Each row in `x` is expected to match at most 1 row in `y`.
## ℹ Row 1 of `x` matches multiple rows.
## ℹ If multiple matches are expected, set `multiple = "all"` to silence this
## warning.
##
##
## ***Differences of at least 0 and at least 0.1%
##
## [1] state name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
##
##
##
## Raw file for vax:
## Rows: 38,104
## Columns: 109
## $ date <date> 2023-03-29, 2023-03-29, 2023-0…
## $ MMWR_week <dbl> 13, 13, 13, 13, 13, 13, 13, 13,…
## $ state <chr> "BP2", "AS", "HI", "IH2", "RI",…
## $ Distributed <dbl> 418430, 128480, 4598400, 407234…
## $ Distributed_Janssen <dbl> 16200, 600, 124900, 111800, 905…
## $ Distributed_Moderna <dbl> 172520, 25000, 1503820, 1528780…
## $ Distributed_Pfizer <dbl> 198750, 100770, 2362220, 193963…
## $ Distributed_Novavax <dbl> 0, 0, 4400, 8300, 4400, 3100, 4…
## $ Distributed_Unk_Manuf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ Dist_Per_100K <dbl> 0, 271101, 324775, 195521, 3293…
## $ Distributed_Per_100k_5Plus <dbl> 0, 298028, 345575, 0, 347230, 4…
## $ Distributed_Per_100k_12Plus <dbl> 0, 348024, 379685, 0, 376187, 4…
## $ Distributed_Per_100k_18Plus <dbl> 0, 410021, 412042, 0, 408146, 4…
## $ Distributed_Per_100k_65Plus <dbl> 0, 3919460, 1712960, 0, 1865370…
## $ vxa <dbl> 347647, 115241, 3507351, 255996…
## $ Administered_5Plus <dbl> 347645, 115218, 3484460, 254425…
## $ Administered_12Plus <dbl> 347645, 102427, 3345431, 243141…
## $ Administered_18Plus <dbl> 347645, 85702, 3132541, 2221391…
## $ Administered_65Plus <dbl> 11918, 9482, 987470, 478068, 72…
## $ Administered_Janssen <dbl> 14831, 580, 71624, 42014, 67173…
## $ Administered_Moderna <dbl> 156444, 25359, 1170251, 1098391…
## $ Administered_Pfizer <dbl> 166491, 87454, 1952507, 1249079…
## $ Administered_Novavax <dbl> 0, 0, 358, 62, 475, 228, 1719, …
## $ Administered_Unk_Manuf <dbl> 38, 1280, 1264, 1168, 2958, 566…
## $ Admin_Per_100k <dbl> 0, 243166, 247717, 122909, 2510…
## $ Admin_Per_100k_5Plus <dbl> 0, 267265, 261861, 0, 262891, 3…
## $ Admin_Per_100k_12Plus <dbl> 0, 277452, 276229, 0, 275080, 3…
## $ Admin_Per_100k_18Plus <dbl> 0, 273502, 280693, 0, 280703, 3…
## $ Admin_Per_100k_65Plus <dbl> 0, 289262, 367844, 0, 389569, 4…
## $ Recip_Administered <dbl> 347647, 115689, 3542243, 255996…
## $ Administered_Dose1_Recip <dbl> 155680, 46206, 1296078, 1164523…
## $ Administered_Dose1_Pop_Pct <dbl> 0.0, 95.0, 91.5, 55.9, 95.0, 95…
## $ Administered_Dose1_Recip_5Plus <dbl> 155678, 46185, 1284179, 1155760…
## $ Administered_Dose1_Recip_5PlusPop_Pct <dbl> 0.0, 95.0, 95.0, 0.0, 95.0, 95.…
## $ Administered_Dose1_Recip_12Plus <dbl> 155678, 39582, 1219812, 1098519…
## $ Administered_Dose1_Recip_12PlusPop_Pct <dbl> 0.0, 95.0, 95.0, 0.0, 95.0, 95.…
## $ Administered_Dose1_Recip_18Plus <dbl> 155678, 32786, 1128879, 998919,…
## $ Administered_Dose1_Recip_18PlusPop_Pct <dbl> 0.0, 95.0, 95.0, 0.0, 95.0, 95.…
## $ Administered_Dose1_Recip_65Plus <dbl> 4224, 3313, 281004, 187075, 234…
## $ Administered_Dose1_Recip_65PlusPop_Pct <dbl> 0.0, 95.0, 95.0, 0.0, 95.0, 95.…
## $ vxc <dbl> 141689, 42479, 1155548, 919151,…
## $ vxcpoppct <dbl> 0.0, 89.6, 81.6, 44.1, 87.9, 90…
## $ Series_Complete_5Plus <dbl> 141688, 42478, 1149155, 916394,…
## $ Series_Complete_5PlusPop_Pct <dbl> 0.0, 95.0, 86.4, 0.0, 92.1, 95.…
## $ Series_Complete_12Plus <dbl> 141688, 36383, 1091967, 872683,…
## $ Series_Complete_12PlusPop_Pct <dbl> 0.0, 95.0, 90.2, 0.0, 95.0, 95.…
## $ vxcgte18 <dbl> 141688, 29946, 1009600, 792582,…
## $ vxcgte18pct <dbl> 0.0, 95.0, 90.5, 0.0, 95.0, 95.…
## $ vxcgte65 <dbl> 3819, 2994, 257765, 148580, 200…
## $ vxcgte65pct <dbl> 0.0, 91.3, 95.0, 0.0, 95.0, 95.…
## $ Series_Complete_Janssen <dbl> 14150, 585, 66365, 39327, 61635…
## $ Series_Complete_Moderna <dbl> 57123, 9540, 380958, 410201, 31…
## $ Series_Complete_Pfizer <dbl> 70416, 31720, 704866, 468368, 5…
## $ Series_Complete_Novavax <dbl> 0, 0, 133, 18, 125, 86, 630, 32…
## $ Series_Complete_Unk_Manuf <dbl> 0, 634, 354, 73, 777, 1438, 559…
## $ Series_Complete_Janssen_5Plus <dbl> 14149, 585, 66337, 39300, 61634…
## $ Series_Complete_Moderna_5Plus <dbl> 57123, 9540, 379050, 409220, 30…
## $ Series_Complete_Pfizer_5Plus <dbl> 70416, 31719, 703282, 467783, 5…
## $ Series_Complete_Unk_Manuf_5Plus <dbl> 0, 634, 353, 73, 760, 1411, 550…
## $ Series_Complete_Janssen_12Plus <dbl> 14149, 585, 66334, 39299, 61634…
## $ Series_Complete_Moderna_12Plus <dbl> 57123, 9539, 378746, 408993, 30…
## $ Series_Complete_Pfizer_12Plus <dbl> 70416, 25626, 646452, 424304, 5…
## $ Series_Complete_Unk_Manuf_12Plus <dbl> 0, 633, 304, 69, 716, 1139, 526…
## $ Series_Complete_Janssen_18Plus <dbl> 14149, 584, 66144, 39254, 61604…
## $ Series_Complete_Moderna_18Plus <dbl> 57123, 9516, 377810, 408550, 30…
## $ Series_Complete_Pfizer_18Plus <dbl> 70416, 19217, 565253, 344693, 4…
## $ Series_Complete_Unk_Manuf_18Plus <dbl> 0, 629, 264, 69, 667, 842, 499,…
## $ Series_Complete_Janssen_65Plus <dbl> 201, 28, 11854, 3508, 6866, 321…
## $ Series_Complete_Moderna_65Plus <dbl> 1837, 1217, 113726, 84001, 8940…
## $ Series_Complete_Pfizer_65Plus <dbl> 1781, 1732, 132111, 61063, 1040…
## $ Series_Complete_Unk_Manuf_65Plus <dbl> 0, 17, 36, 5, 183, 219, 97, 128…
## $ Additional_Doses <dbl> 53622, 24680, 690868, 345445, 5…
## $ Additional_Doses_Vax_Pct <dbl> 37.8, 58.1, 59.8, 37.6, 58.4, 5…
## $ Additional_Doses_5Plus <dbl> 53622, 24680, 690646, 345311, 5…
## $ Additional_Doses_5Plus_Vax_Pct <dbl> 37.8, 58.1, 60.1, 37.7, 58.7, 5…
## $ Additional_Doses_12Plus <dbl> 53622, 24648, 676291, 335147, 5…
## $ Additional_Doses_12Plus_Vax_Pct <dbl> 37.8, 67.7, 61.9, 38.4, 60.6, 5…
## $ Additional_Doses_18Plus <dbl> 53622, 21198, 641485, 310374, 5…
## $ Additional_Doses_18Plus_Vax_Pct <dbl> 37.8, 70.8, 63.5, 39.2, 62.0, 5…
## $ Additional_Doses_50Plus <dbl> 16806, 8437, 396324, 182866, 30…
## $ Additional_Doses_50Plus_Vax_Pct <dbl> 54.9, 79.6, 77.4, 47.7, 73.5, 6…
## $ Additional_Doses_65Plus <dbl> 2697, 2412, 218746, 82374, 1627…
## $ Additional_Doses_65Plus_Vax_Pct <dbl> 70.6, 80.6, 84.9, 55.4, 81.1, 7…
## $ Additional_Doses_Moderna <dbl> 31606, 4945, 288011, 154519, 22…
## $ Additional_Doses_Pfizer <dbl> 21350, 19729, 396026, 188102, 3…
## $ Additional_Doses_Janssen <dbl> 643, 2, 6539, 2525, 5352, 2113,…
## $ Additional_Doses_Unk_Manuf <dbl> 23, 4, 245, 294, 210, 501, 247,…
## $ Second_Booster <dbl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Second_Booster_50Plus <dbl> 5026, 2274, 239196, 80425, 1721…
## $ Second_Booster_50Plus_Vax_Pct <dbl> 29.9, 27.0, 60.4, 44.0, 55.8, 5…
## $ Second_Booster_65Plus <dbl> 1115, 875, 149385, 41110, 10615…
## $ Second_Booster_65Plus_Vax_Pct <dbl> 41.3, 36.3, 68.3, 49.9, 65.2, 6…
## $ Second_Booster_Janssen <dbl> 3, 0, 123, 21, 178, 93, 196, 26…
## $ Second_Booster_Moderna <dbl> 2634, 532, 144967, 48240, 10103…
## $ Second_Booster_Pfizer <dbl> 6153, 1907, 161732, 65694, 1262…
## $ Second_Booster_Unk_Manuf <dbl> 3, 0, 54, 256, 108, 373, 61, 22…
## $ Administered_Bivalent <dbl> 9837, 525, 311091, 169098, 2565…
## $ Admin_Bivalent_PFR <dbl> 9507, 328, 169384, 107715, 1438…
## $ Admin_Bivalent_MOD <dbl> 330, 197, 141707, 61383, 112675…
## $ Dist_Bivalent_PFR <dbl> 19560, 1610, 357640, 321450, 29…
## $ Dist_Bivalent_MOD <dbl> 11400, 500, 245420, 162380, 189…
## $ Bivalent_Booster_5Plus <dbl> 9773, 563, 312151, 168051, 2664…
## $ Bivalent_Booster_5Plus_Pop_Pct <dbl> 0.0, 1.3, 23.5, 0.0, 26.5, 33.4…
## $ Bivalent_Booster_12Plus <dbl> 9773, 560, 304879, 161297, 2607…
## $ Bivalent_Booster_12Plus_Pop_Pct <dbl> 0.0, 1.5, 25.2, 0.0, 28.1, 34.9…
## $ Bivalent_Booster_18Plus <dbl> 9773, 555, 294343, 149113, 2519…
## $ Bivalent_Booster_18Plus_Pop_Pct <dbl> 0.0, 1.8, 26.4, 0.0, 29.5, 35.6…
## $ Bivalent_Booster_65Plus <dbl> 677, 197, 138250, 45820, 109055…
## $ Bivalent_Booster_65Plus_Pop_Pct <dbl> 0.0, 6.0, 51.5, 0.0, 58.3, 55.7…
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 6
## isType tot_cases tot_deaths new_cases new_deaths n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 8.02e+9 1.06e+8 1.04e+8 1121270 9853
## 2 after 7.95e+9 1.05e+8 1.03e+8 1114704 8517
## 3 pctchg 8.63e-3 4.92e-3 1.21e-2 0.00586 0.136## Warning: Using `all_of()` outside of a selecting function was deprecated in tidyselect
## 1.2.0.
## ℹ See details at
## <https://tidyselect.r-lib.org/reference/faq-selection-context.html>## Warning: Using `by = character()` to perform a cross join was deprecated in dplyr 1.1.0.
## ℹ Please use `cross_join()` instead.##
## Processed for cdcWeeklyBurden:
## Rows: 59,313
## Columns: 6
## $ date <date> 2020-01-22, 2020-01-23, 2020-01-24, 2020-01-25, 2020-01-26…
## $ state <chr> "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK",…
## $ tot_cases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tot_deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ new_cases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ new_deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 5
## isType inp hosp_adult hosp_ped n
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 before 5.76e+7 5.07e+7 1511938 60599
## 2 after 5.73e+7 5.04e+7 1483681 57789
## 3 pctchg 5.37e-3 5.12e-3 0.0187 0.0464
##
##
## Processed for cdcHosp:
## Rows: 57,789
## Columns: 5
## $ date <date> 2021-02-18, 2021-02-13, 2021-02-05, 2021-02-02, 2021-01-26…
## $ state <chr> "SD", "NM", "CT", "MA", "ME", "MD", "RI", "MT", "DE", "ND",…
## $ inp <dbl> 90, 334, 979, 1518, 221, 2180, 423, 181, 510, 162, 434, 449…
## $ hosp_adult <dbl> 87, 327, 973, 1501, 220, 2161, 416, 178, 501, 159, 432, 446…
## $ hosp_ped <dbl> 3, 7, 6, 17, 1, 19, 7, 3, 9, 3, 2, 3, 24, 10, 73, 15, 5, 4,…
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 9
## isType vxa vxc vxcpoppct vxcgte65 vxcgt…¹ vxcgte18 vxcgt…² n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 4.54e+11 1.84e+11 1636331. 4.63e+10 2.40e+6 1.70e+11 1.92e+6 3.81e+4
## 2 after 2.19e+11 8.89e+10 1368749. 2.24e+10 2.12e+6 8.21e+10 1.63e+6 3.01e+4
## 3 pctchg 5.18e- 1 5.16e- 1 0.164 5.16e- 1 1.16e-1 5.16e- 1 1.54e-1 2.09e-1
## # … with abbreviated variable names ¹vxcgte65pct, ²vxcgte18pct
##
##
## Processed for vax:
## Rows: 42,636
## Columns: 9
## $ date <date> 2020-12-14, 2020-12-15, 2020-12-16, 2020-12-17, 2020-12-1…
## $ state <chr> "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK"…
## $ vxa <dbl> 0, 0, 0, 2, 2, 1607, 4239, 5125, 5615, 6822, 8578, 10612, …
## $ vxc <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ vxcpoppct <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ vxcgte65 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ vxcgte65pct <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ vxcgte18 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ vxcgte18pct <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
##
## Integrated per capita data file:
## Rows: 59,679
## Columns: 34
## $ date <date> 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-0…
## $ state <chr> "AL", "HI", "IN", "LA", "MN", "MT", "NC", "TX", "AL", "HI"…
## $ tot_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tot_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ new_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ new_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ inp <dbl> NA, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 1877, 0, …
## $ hosp_adult <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hosp_ped <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxa <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxc <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpoppct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte65 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte65pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte18 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte18pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tdpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ cpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ dpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hpm <dbl> NA, 0.0000, 0.0000, NA, 0.0000, 0.0000, 0.0000, 0.0000, NA…
## $ ahpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ phpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxapm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tdpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ cpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ dpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ ahpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ phpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxapm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.## Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
saveToRDS(cdc_daily_230402, ovrWriteError=FALSE)##
## File already exists: ./RInputFiles/Coronavirus/cdc_daily_230402.RDS
##
## Not replacing the existing file since ovrWrite=FALSE## NULLThe latest hospitalization data is also downloaded and processed:
# Run for latest data, save as RDS
indivHosp_20230403 <- downloadReadHospitalData(loc="./RInputFiles/Coronavirus/HHS_Hospital_20230403.csv")##
## File ./RInputFiles/Coronavirus/HHS_Hospital_20230403.csv already exists
## File will not be downloaded since ovrWrite is not TRUE## Rows: 261207 Columns: 128
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (11): hospital_pk, state, ccn, hospital_name, address, city, zip, hosp...
## dbl (114): total_beds_7_day_avg, all_adult_hospital_beds_7_day_avg, all_adu...
## lgl (2): is_metro_micro, is_corrected
## date (1): collection_week
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.## Rows: 261,207
## Columns: 128
## $ hospital_pk <chr> …
## $ collection_week <date> …
## $ state <chr> …
## $ ccn <chr> …
## $ hospital_name <chr> …
## $ address <chr> …
## $ city <chr> …
## $ zip <chr> …
## $ hospital_subtype <chr> …
## $ fips_code <chr> …
## $ is_metro_micro <lgl> …
## $ total_beds_7_day_avg <dbl> …
## $ all_adult_hospital_beds_7_day_avg <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_avg <dbl> …
## $ inpatient_beds_used_7_day_avg <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_avg <dbl> …
## $ inpatient_beds_used_covid_7_day_avg <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_avg <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_avg <dbl> …
## $ inpatient_beds_7_day_avg <dbl> …
## $ total_icu_beds_7_day_avg <dbl> …
## $ total_staffed_adult_icu_beds_7_day_avg <dbl> …
## $ icu_beds_used_7_day_avg <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_avg <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_avg <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_avg <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_avg <dbl> …
## $ icu_patients_confirmed_influenza_7_day_avg <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_avg <dbl> …
## $ total_beds_7_day_sum <dbl> …
## $ all_adult_hospital_beds_7_day_sum <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_sum <dbl> …
## $ inpatient_beds_used_7_day_sum <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_sum <dbl> …
## $ inpatient_beds_used_covid_7_day_sum <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_sum <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_sum <dbl> …
## $ inpatient_beds_7_day_sum <dbl> …
## $ total_icu_beds_7_day_sum <dbl> …
## $ total_staffed_adult_icu_beds_7_day_sum <dbl> …
## $ icu_beds_used_7_day_sum <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_sum <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_sum <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_sum <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_sum <dbl> …
## $ icu_patients_confirmed_influenza_7_day_sum <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_sum <dbl> …
## $ total_beds_7_day_coverage <dbl> …
## $ all_adult_hospital_beds_7_day_coverage <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_coverage <dbl> …
## $ inpatient_beds_used_7_day_coverage <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_coverage <dbl> …
## $ inpatient_beds_used_covid_7_day_coverage <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_coverage <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_coverage <dbl> …
## $ inpatient_beds_7_day_coverage <dbl> …
## $ total_icu_beds_7_day_coverage <dbl> …
## $ total_staffed_adult_icu_beds_7_day_coverage <dbl> …
## $ icu_beds_used_7_day_coverage <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_coverage <dbl> …
## $ icu_patients_confirmed_influenza_7_day_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_coverage <dbl> …
## $ previous_day_admission_adult_covid_confirmed_7_day_sum <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+_7_day_sum` <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_7_day_sum <dbl> …
## $ previous_day_covid_ED_visits_7_day_sum <dbl> …
## $ previous_day_admission_adult_covid_suspected_7_day_sum <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+_7_day_sum` <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_7_day_sum <dbl> …
## $ previous_day_total_ED_visits_7_day_sum <dbl> …
## $ previous_day_admission_influenza_confirmed_7_day_sum <dbl> …
## $ geocoded_hospital_address <chr> …
## $ hhs_ids <chr> …
## $ previous_day_admission_adult_covid_confirmed_7_day_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_7_day_coverage <dbl> …
## $ previous_day_admission_adult_covid_suspected_7_day_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_7_day_coverage <dbl> …
## $ previous_week_personnel_covid_vaccinated_doses_administered_7_day <dbl> …
## $ total_personnel_covid_vaccinated_doses_none_7_day <dbl> …
## $ total_personnel_covid_vaccinated_doses_one_7_day <dbl> …
## $ total_personnel_covid_vaccinated_doses_all_7_day <dbl> …
## $ previous_week_patients_covid_vaccinated_doses_one_7_day <dbl> …
## $ previous_week_patients_covid_vaccinated_doses_all_7_day <dbl> …
## $ is_corrected <lgl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_avg <dbl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_coverage <dbl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_sum <dbl> …
## $ all_pediatric_inpatient_beds_7_day_avg <dbl> …
## $ all_pediatric_inpatient_beds_7_day_coverage <dbl> …
## $ all_pediatric_inpatient_beds_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown_7_day_sum <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_avg <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_coverage <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_sum <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_avg <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_coverage <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_sum <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_avg <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_coverage <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_sum <dbl> …
##
## Hospital Subtype Counts:
## # A tibble: 4 × 2
## hospital_subtype n
## <chr> <int>
## 1 Childrens Hospitals 4857
## 2 Critical Access Hospitals 70233
## 3 Long Term 17536
## 4 Short Term 168581
##
## Records other than 50 states and DC
## # A tibble: 5 × 2
## state n
## <chr> <int>
## 1 AS 29
## 2 GU 123
## 3 MP 40
## 4 PR 2556
## 5 VI 98
##
## Record types for key metrics## Warning: Specifying the `id_cols` argument by position was deprecated in tidyr 1.3.0.
## ℹ Please explicitly name `id_cols`, like `id_cols = name`.## # A tibble: 10 × 5
## name `NA` Posit…¹ Value…² Total
## <chr> <int> <int> <int> <int>
## 1 all_adult_hospital_beds_7_day_avg 77420 183398 389 261207
## 2 all_adult_hospital_inpatient_bed_occupied_7_day… 30117 211729 19361 261207
## 3 icu_beds_used_7_day_avg 33204 199916 28087 261207
## 4 inpatient_beds_7_day_avg 8352 251849 1006 261207
## 5 inpatient_beds_used_7_day_avg 8352 231713 21142 261207
## 6 inpatient_beds_used_covid_7_day_avg 1763 173895 85549 261207
## 7 staffed_icu_adult_patients_confirmed_and_suspec… 30086 156464 74657 261207
## 8 total_adult_patients_hospitalized_confirmed_and… 27571 157031 76605 261207
## 9 total_beds_7_day_avg 53399 207570 238 261207
## 10 total_icu_beds_7_day_avg 3330 244695 13182 261207
## # … with abbreviated variable names ¹Positive, ²`Value -999999`## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
saveToRDS(indivHosp_20230403, ovrWriteError=FALSE)##
## File already exists: ./RInputFiles/Coronavirus/indivHosp_20230403.RDS
##
## Not replacing the existing file since ovrWrite=FALSE## NULLPost-processing is run, including hospital summaries:
# Create pivoted burden data
burdenPivotList_230402 <- postProcessCDCDaily(cdc_daily_230402,
dataThruLabel="Mar 2023",
keyDatesBurden=c("2023-03-29", "2022-06-30",
"2021-12-31", "2021-06-30"
),
keyDatesVaccine=c("2023-03-29", "2021-12-31",
"2021-08-31", "2021-03-31"
),
returnData=TRUE
)## Joining with `by = join_by(state)`##
## *** File has been checked for uniqueness by: state date name## Warning: Removed 24 rows containing missing values (`geom_line()`).
## Warning: Removed 24 rows containing missing values (`position_stack()`).
## Warning: Removed 24 rows containing missing values (`position_stack()`).
## Warning: Removed 9 rows containing missing values (`geom_line()`).## Warning: Specifying the `id_cols` argument by position was deprecated in tidyr 1.3.0.
## ℹ Please explicitly name `id_cols`, like `id_cols = state`.## Warning: Specifying the `id_cols` argument by position was deprecated in tidyr 1.3.0.
## ℹ Please explicitly name `id_cols`, like `id_cols = state`.
## Warning: Specifying the `id_cols` argument by position was deprecated in tidyr 1.3.0.
## ℹ Please explicitly name `id_cols`, like `id_cols = state`.## Warning: Specifying the `id_cols` argument by position was deprecated in tidyr 1.3.0.
## ℹ Please explicitly name `id_cols`, like `id_cols = state`.
## Warning: Specifying the `id_cols` argument by position was deprecated in tidyr 1.3.0.
## ℹ Please explicitly name `id_cols`, like `id_cols = state`.## Warning: Specifying the `id_cols` argument by position was deprecated in tidyr 1.3.0.
## ℹ Please explicitly name `id_cols`, like `id_cols = state`.
# Create hospitalized per capita data
hospPerCap_230402 <- hospAgePerCapita(readFromRDS("dfStateAgeBucket2019"),
lst=burdenPivotList_230402,
popVar="pop2019",
excludeState=c(),
cumStartDate="2020-07-15"
)## Warning: Removed 18 rows containing missing values (`geom_line()`).
burdenPivotList_230402$hospAge %>%
group_by(adultPed, confSusp, age, name) %>%
summarize(value=sum(value, na.rm=TRUE), n=n(), .groups="drop")## # A tibble: 18 × 6
## adultPed confSusp age name value n
## <chr> <chr> <chr> <chr> <dbl> <int>
## 1 adult confirmed 0-19 previous_day_admission_adult_covid_con… 5.13e4 60599
## 2 adult confirmed 20-29 previous_day_admission_adult_covid_con… 3.20e5 60599
## 3 adult confirmed 30-39 previous_day_admission_adult_covid_con… 4.59e5 60599
## 4 adult confirmed 40-49 previous_day_admission_adult_covid_con… 5.40e5 60599
## 5 adult confirmed 50-59 previous_day_admission_adult_covid_con… 8.69e5 60599
## 6 adult confirmed 60-69 previous_day_admission_adult_covid_con… 1.18e6 60599
## 7 adult confirmed 70-79 previous_day_admission_adult_covid_con… 1.23e6 60599
## 8 adult confirmed 80+ previous_day_admission_adult_covid_con… 1.17e6 60599
## 9 adult suspected 0-19 previous_day_admission_adult_covid_sus… 4.47e4 60599
## 10 adult suspected 20-29 previous_day_admission_adult_covid_sus… 2.98e5 60599
## 11 adult suspected 30-39 previous_day_admission_adult_covid_sus… 3.93e5 60599
## 12 adult suspected 40-49 previous_day_admission_adult_covid_sus… 3.98e5 60599
## 13 adult suspected 50-59 previous_day_admission_adult_covid_sus… 6.32e5 60599
## 14 adult suspected 60-69 previous_day_admission_adult_covid_sus… 8.86e5 60599
## 15 adult suspected 70-79 previous_day_admission_adult_covid_sus… 8.73e5 60599
## 16 adult suspected 80+ previous_day_admission_adult_covid_sus… 8.03e5 60599
## 17 ped confirmed 0-19 previous_day_admission_pediatric_covid… 2.03e5 60599
## 18 ped suspected 0-19 previous_day_admission_pediatric_covid… 4.75e5 60599saveToRDS(burdenPivotList_230402, ovrWriteError=FALSE)##
## File already exists: ./RInputFiles/Coronavirus/burdenPivotList_230402.RDS
##
## Not replacing the existing file since ovrWrite=FALSE## NULLsaveToRDS(hospPerCap_230402, ovrWriteError=FALSE)##
## File already exists: ./RInputFiles/Coronavirus/hospPerCap_230402.RDS
##
## Not replacing the existing file since ovrWrite=FALSE## NULLPeaks and valleys of key metrics are also updated:
peakValleyCDCDaily(cdc_daily_230402)## Warning: Removed 6 rows containing missing values (`geom_line()`).
## Warning: Removed 6 rows containing missing values (`geom_line()`).
## Warning: Removed 6 rows containing missing values (`geom_line()`).
## Warning: Removed 20 rows containing missing values (`geom_line()`).
## Warning: Removed 20 rows containing missing values (`geom_line()`).
## # A tibble: 10,236 × 8
## date state vxa vxc vxa_isPeak vxc_isPeak vxa_isValley vxc_isValley
## <date> <chr> <dbl> <dbl> <lgl> <lgl> <lgl> <lgl>
## 1 2020-12-01 CA NA NA FALSE FALSE FALSE FALSE
## 2 2020-12-01 FL NA NA FALSE FALSE FALSE FALSE
## 3 2020-12-01 GA NA NA FALSE FALSE FALSE FALSE
## 4 2020-12-01 IL NA NA FALSE FALSE FALSE FALSE
## 5 2020-12-01 MI NA NA FALSE FALSE FALSE FALSE
## 6 2020-12-01 NC NA NA FALSE FALSE FALSE FALSE
## 7 2020-12-01 NJ NA NA FALSE FALSE FALSE FALSE
## 8 2020-12-01 NY NA NA FALSE FALSE FALSE FALSE
## 9 2020-12-01 OH NA NA FALSE FALSE FALSE FALSE
## 10 2020-12-01 PA NA NA FALSE FALSE FALSE FALSE
## # … with 10,226 more rowsHospital data are pieced together as needed:
# Create modified hospital data
multiSourceHosp_20230402 <- multiSourceDataCombine(list(readFromRDS("indivHosp_20220704"),
readFromRDS("indivHosp_20230403")
),
timeVec=as.Date("2022-01-01")
)The updated hospital data are then plotted:
# Run hospital plots
modStateHosp_20230402 <- hospitalCapacityCDCDaily(multiSourceHosp_20230402,
plotSub="Aug 2020 to Mar 2023\nOld data used pre-2022"
)
Data Refreshes
Data From 2023-05-02
The latest data are downloaded and processed:
readList <- list("cdcWeeklyBurden"="./RInputFiles/Coronavirus/CDC_dc_wkly_downloaded_230502.csv",
"cdcHosp"="./RInputFiles/Coronavirus/CDC_h_downloaded_230502.csv",
"vax"="./RInputFiles/Coronavirus/vaxData_downloaded_230502.csv"
)
compareList <- list("cdcWeeklyBurden"=readFromRDS("cdc_daily_230402")$dfRaw$cdcWeeklyBurden,
"cdcHosp"=readFromRDS("cdc_daily_230402")$dfRaw$cdcHosp,
"vax"=readFromRDS("cdc_daily_230402")$dfRaw$vax
)
cdc_daily_230502 <- readRunCDCDaily(thruLabel="Apr 30, 2023",
downloadTo=lapply(readList, FUN=function(x) if(file.exists(x)) NA else x),
readFrom=readList,
compareFile=compareList,
writeLog=NULL,
useClusters=readFromRDS("cdc_daily_210528")$useClusters,
weightedMeanAggs=c("tcpm7", "tdpm7", "cpm7", "dpm7", "hpm7",
"vxcpm7", "vxcgte65pct"
),
skipAssessmentPlots=FALSE,
brewPalette="Paired"
)## Rows: 10260 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): date_updated, state, start_date, end_date
## dbl (6): tot_cases, new_cases, tot_deaths, new_deaths, new_historic_cases, n...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.##
## *** File has been checked for uniqueness by: state date## Warning: There was 1 warning in `summarize()`.
## ℹ In argument: `across(.cols = all_of(useVars), .fns = fn, ...)`.
## ℹ In group 1: `date = 2020-01-22`.
## Caused by warning:
## ! The `...` argument of `across()` is deprecated as of dplyr 1.1.0.
## Supply arguments directly to `.fns` through an anonymous function instead.
##
## # Previously
## across(a:b, mean, na.rm = TRUE)
##
## # Now
## across(a:b, \(x) mean(x, na.rm = TRUE))## Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
## ℹ Please use tidy evaluation ideoms with `aes()`
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 4
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2020-03-11 tot_deaths 33 14 19 0.80851064
## 2 2020-03-18 tot_deaths 202 140 62 0.36257310
## 3 2020-03-25 tot_deaths 1387 1259 128 0.09674981
## 4 2020-02-26 tot_cases 80 72 8 0.10526316
## 5 2020-03-11 new_deaths 22 8 14 0.93333333
## 6 2021-12-08 new_deaths 8816 6465 2351 0.30770238
## 7 2020-03-18 new_deaths 169 126 43 0.29152542
## 8 2022-05-04 new_deaths 2427 2742 315 0.12188044
## 9 2022-05-11 new_deaths 2157 2401 244 0.10706450
## 10 2022-04-20 new_deaths 2716 2955 239 0.08428849
## 11 2022-04-27 new_deaths 2676 2903 227 0.08137659
## 12 2022-08-10 new_deaths 3377 3624 247 0.07056135
## 13 2022-05-18 new_deaths 2122 2275 153 0.06959290
## 14 2022-08-03 new_deaths 3369 3572 203 0.05849301
## 15 2020-03-25 new_deaths 1185 1119 66 0.05729167
## 16 2022-05-25 new_deaths 2511 2648 137 0.05311107
## 17 2022-06-01 new_deaths 1896 1994 98 0.05038560
## 18 2020-03-04 new_cases 97 108 11 0.10731707## Warning in left_join(., df, by = names(univData)): Each row in `x` is expected to match at most 1 row in `y`.
## ℹ Row 1 of `x` matches multiple rows.
## ℹ If multiple matches are expected, set `multiple = "all"` to silence this
## warning.## Warning in left_join(., ref, by = names(univData)): Each row in `x` is expected to match at most 1 row in `y`.
## ℹ Row 1 of `x` matches multiple rows.
## ℹ If multiple matches are expected, set `multiple = "all"` to silence this
## warning.
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 WA tot_deaths 1292526 1139242 153284 0.126067947
## 2 AR tot_cases 80951600 82279688 1328088 0.016272469
## 3 MO new_deaths 22734 20311 2423 0.112579858
## 4 DE new_deaths 3379 3352 27 0.008022582
## 5 OK new_deaths 15572 15612 40 0.002565418
## 6 MS new_deaths 13419 13402 17 0.001267663
## 7 VT new_deaths 942 941 1 0.001062135
## 8 AR new_cases 990092 1007357 17265 0.017287050
## 9 KY new_cases 1732457 1724772 7685 0.004445757
## 10 OK new_cases 1293792 1295832 2040 0.001575518
## 11 MS new_cases 994313 993035 1278 0.001286136
##
##
##
## Raw file for cdcWeeklyBurden:
## Rows: 10,260
## Columns: 10
## $ date_updated <chr> "01/23/2020", "01/30/2020", "02/06/2020", "02/13/2…
## $ state <chr> "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", "A…
## $ start_date <chr> "01/16/2020", "01/23/2020", "01/30/2020", "02/06/2…
## $ date <date> 2020-01-22, 2020-01-29, 2020-02-05, 2020-02-12, 2…
## $ tot_cases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 11, 63, 149, 235, 300, 337…
## $ new_cases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 11, 52, 86, 86, 65, 37, 18…
## $ tot_deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 7, 9, 9, 9, 10, 1…
## $ new_deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 4, 2, 0, 0, 1, 0,…
## $ new_historic_cases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ new_historic_deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…## Rows: 62162 Columns: 135
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): state
## dbl (132): critical_staffing_shortage_today_yes, critical_staffing_shortage...
## lgl (1): geocoded_state
## date (1): date
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 29
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2023-04-01 hosp_ped 812 901 89 0.10391127
## 2 2023-04-02 hosp_ped 825 898 73 0.08473593## Warning in left_join(., df, by = names(univData)): Each row in `x` is expected to match at most 1 row in `y`.
## Each row in `x` is expected to match at most 1 row in `y`.
## ℹ Row 1 of `x` matches multiple rows.
## ℹ If multiple matches are expected, set `multiple = "all"` to silence this
## warning.
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 VI inp 6142 6274 132 0.021262887
## 2 VI hosp_ped 147 159 12 0.078431373
## 3 NV hosp_ped 6991 7029 38 0.005420827
## 4 VT hosp_ped 855 859 4 0.004667445
## 5 AK hosp_ped 3283 3294 11 0.003344990
## 6 DE hosp_ped 9129 9156 27 0.002953240
## 7 KY hosp_ped 28401 28484 83 0.002918168
## 8 MS hosp_ped 15392 15371 21 0.001365276
## 9 WY hosp_ped 990 991 1 0.001009591
## 10 VI hosp_adult 5830 5950 120 0.020373514
##
##
##
## Raw file for cdcHosp:
## Rows: 62,162
## Columns: 135
## $ state <chr> …
## $ date <date> …
## $ critical_staffing_shortage_today_yes <dbl> …
## $ critical_staffing_shortage_today_no <dbl> …
## $ critical_staffing_shortage_today_not_reported <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_yes <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_no <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_not_reported <dbl> …
## $ hospital_onset_covid <dbl> …
## $ hospital_onset_covid_coverage <dbl> …
## $ inpatient_beds <dbl> …
## $ inpatient_beds_coverage <dbl> …
## $ inpatient_beds_used <dbl> …
## $ inpatient_beds_used_coverage <dbl> …
## $ inp <dbl> …
## $ inpatient_beds_used_covid_coverage <dbl> …
## $ previous_day_admission_adult_covid_confirmed <dbl> …
## $ previous_day_admission_adult_covid_confirmed_coverage <dbl> …
## $ previous_day_admission_adult_covid_suspected <dbl> …
## $ previous_day_admission_adult_covid_suspected_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_suspected <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_coverage <dbl> …
## $ staffed_adult_icu_bed_occupancy <dbl> …
## $ staffed_adult_icu_bed_occupancy_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_coverage <dbl> …
## $ hosp_adult <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_coverage <dbl> …
## $ hosp_ped <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_coverage <dbl> …
## $ total_staffed_adult_icu_beds <dbl> …
## $ total_staffed_adult_icu_beds_coverage <dbl> …
## $ inpatient_beds_utilization <dbl> …
## $ inpatient_beds_utilization_coverage <dbl> …
## $ inpatient_beds_utilization_numerator <dbl> …
## $ inpatient_beds_utilization_denominator <dbl> …
## $ percent_of_inpatients_with_covid <dbl> …
## $ percent_of_inpatients_with_covid_coverage <dbl> …
## $ percent_of_inpatients_with_covid_numerator <dbl> …
## $ percent_of_inpatients_with_covid_denominator <dbl> …
## $ inpatient_bed_covid_utilization <dbl> …
## $ inpatient_bed_covid_utilization_coverage <dbl> …
## $ inpatient_bed_covid_utilization_numerator <dbl> …
## $ inpatient_bed_covid_utilization_denominator <dbl> …
## $ adult_icu_bed_covid_utilization <dbl> …
## $ adult_icu_bed_covid_utilization_coverage <dbl> …
## $ adult_icu_bed_covid_utilization_numerator <dbl> …
## $ adult_icu_bed_covid_utilization_denominator <dbl> …
## $ adult_icu_bed_utilization <dbl> …
## $ adult_icu_bed_utilization_coverage <dbl> …
## $ adult_icu_bed_utilization_numerator <dbl> …
## $ adult_icu_bed_utilization_denominator <dbl> …
## $ geocoded_state <lgl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+_coverage` <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown_coverage <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+_coverage` <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown_coverage <dbl> …
## $ deaths_covid <dbl> …
## $ deaths_covid_coverage <dbl> …
## $ on_hand_supply_therapeutic_a_casirivimab_imdevimab_courses <dbl> …
## $ on_hand_supply_therapeutic_b_bamlanivimab_courses <dbl> …
## $ on_hand_supply_therapeutic_c_bamlanivimab_etesevimab_courses <dbl> …
## $ previous_week_therapeutic_a_casirivimab_imdevimab_courses_used <dbl> …
## $ previous_week_therapeutic_b_bamlanivimab_courses_used <dbl> …
## $ previous_week_therapeutic_c_bamlanivimab_etesevimab_courses_used <dbl> …
## $ icu_patients_confirmed_influenza <dbl> …
## $ icu_patients_confirmed_influenza_coverage <dbl> …
## $ previous_day_admission_influenza_confirmed <dbl> …
## $ previous_day_admission_influenza_confirmed_coverage <dbl> …
## $ previous_day_deaths_covid_and_influenza <dbl> …
## $ previous_day_deaths_covid_and_influenza_coverage <dbl> …
## $ previous_day_deaths_influenza <dbl> …
## $ previous_day_deaths_influenza_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_coverage <dbl> …
## $ all_pediatric_inpatient_bed_occupied <dbl> …
## $ all_pediatric_inpatient_bed_occupied_coverage <dbl> …
## $ all_pediatric_inpatient_beds <dbl> …
## $ all_pediatric_inpatient_beds_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4 <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17 <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11 <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown_coverage <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_coverage <dbl> …
## $ staffed_pediatric_icu_bed_occupancy <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_coverage <dbl> …
## $ total_staffed_pediatric_icu_beds <dbl> …
## $ total_staffed_pediatric_icu_beds_coverage <dbl> …## Rows: 38360 Columns: 109
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Date, Location
## dbl (107): MMWR_week, Distributed, Distributed_Janssen, Distributed_Moderna,...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 4
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 1 and at least 1%
##
## [1] date name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)## Warning in left_join(., df, by = names(univData)): Each row in `x` is expected to match at most 1 row in `y`.
## Each row in `x` is expected to match at most 1 row in `y`.
## ℹ Row 1 of `x` matches multiple rows.
## ℹ If multiple matches are expected, set `multiple = "all"` to silence this
## warning.
##
##
## ***Differences of at least 0 and at least 0.1%
##
## [1] state name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
##
##
##
## Raw file for vax:
## Rows: 38,360
## Columns: 109
## $ date <date> 2023-04-26, 2023-04-26, 2023-0…
## $ MMWR_week <dbl> 17, 17, 17, 17, 17, 17, 17, 17,…
## $ state <chr> "WI", "MH", "PA", "IA", "MP", "…
## $ Distributed <dbl> 16390495, 162240, 42655195, 936…
## $ Distributed_Janssen <dbl> 457200, 12800, 1569200, 293100,…
## $ Distributed_Moderna <dbl> 5144600, 74500, 13941120, 30779…
## $ Distributed_Pfizer <dbl> 8183105, 53540, 21178525, 45778…
## $ Distributed_Novavax <dbl> 22100, 1100, 77100, 22000, 200,…
## $ Distributed_Unk_Manuf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ Dist_Per_100K <dbl> 281506, 208693, 333192, 296761,…
## $ Distributed_Per_100k_5Plus <dbl> 298446, 234458, 352404, 316379,…
## $ Distributed_Per_100k_12Plus <dbl> 327824, 282801, 384922, 349852,…
## $ Distributed_Per_100k_18Plus <dbl> 359769, 341349, 419530, 385591,…
## $ Distributed_Per_100k_65Plus <dbl> 1611270, 4347270, 1782230, 1693…
## $ vxa <dbl> 12419813, 101022, 27536533, 612…
## $ Administered_5Plus <dbl> 12323643, 97254, 27313374, 6078…
## $ Administered_12Plus <dbl> 11907256, 85856, 26362356, 5882…
## $ Administered_18Plus <dbl> 11242331, 73524, 24878639, 5554…
## $ Administered_65Plus <dbl> 3933430, 4718, 8471200, 2004872…
## $ Administered_Janssen <dbl> 338955, 3099, 803119, 181028, 1…
## $ Administered_Moderna <dbl> 4215718, 66804, 9687787, 217083…
## $ Administered_Pfizer <dbl> 6528721, 24899, 14561549, 31323…
## $ Administered_Novavax <dbl> 1519, 0, 2381, 770, 1, 474, 366…
## $ Administered_Unk_Manuf <dbl> 5153, 5, 1979, 2765, 9, 2788, 1…
## $ Admin_Per_100k <dbl> 213310, 129947, 215096, 194031,…
## $ Admin_Per_100k_5Plus <dbl> 224395, 140545, 225655, 205407,…
## $ Admin_Per_100k_12Plus <dbl> 238155, 149656, 237895, 219806,…
## $ Admin_Per_100k_18Plus <dbl> 246768, 154693, 244691, 228743,…
## $ Admin_Per_100k_65Plus <dbl> 386676, 126420, 353946, 362575,…
## $ Recip_Administered <dbl> 12423939, 101077, 27595351, 613…
## $ Administered_Dose1_Recip <dbl> 4385177, 44548, 11654826, 22346…
## $ Administered_Dose1_Pop_Pct <dbl> 75.3, 57.3, 91.0, 70.8, 90.3, 6…
## $ Administered_Dose1_Recip_5Plus <dbl> 4339949, 41959, 11550924, 22149…
## $ Administered_Dose1_Recip_5PlusPop_Pct <dbl> 79.0, 60.6, 95.0, 74.8, 95.0, 7…
## $ Administered_Dose1_Recip_12Plus <dbl> 4155511, 35402, 11098829, 21262…
## $ Administered_Dose1_Recip_12PlusPop_Pct <dbl> 83.1, 61.7, 95.0, 79.4, 95.0, 7…
## $ Administered_Dose1_Recip_18Plus <dbl> 3879760, 29330, 10416722, 19848…
## $ Administered_Dose1_Recip_18PlusPop_Pct <dbl> 85.2, 61.7, 95.0, 81.7, 95.0, 8…
## $ Administered_Dose1_Recip_65Plus <dbl> 1080934, 1626, 2915906, 564261,…
## $ Administered_Dose1_Recip_65PlusPop_Pct <dbl> 95.0, 43.6, 95.0, 95.0, 87.9, 9…
## $ vxc <dbl> 3980224, 35878, 9435863, 203581…
## $ vxcpoppct <dbl> 68.4, 46.2, 73.7, 64.5, 84.9, 5…
## $ Series_Complete_5Plus <dbl> 3953783, 34907, 9365799, 202431…
## $ Series_Complete_5PlusPop_Pct <dbl> 72.0, 50.4, 77.4, 68.4, 91.3, 6…
## $ Series_Complete_12Plus <dbl> 3788449, 30561, 8996519, 194539…
## $ Series_Complete_12PlusPop_Pct <dbl> 75.8, 53.3, 81.2, 72.7, 93.8, 6…
## $ vxcgte18 <dbl> 3538185, 26009, 8440810, 181782…
## $ vxcgte18pct <dbl> 77.7, 54.7, 83.0, 74.9, 95.0, 6…
## $ vxcgte65 <dbl> 1008642, 1458, 2442959, 532654,…
## $ vxcgte65pct <dbl> 95.0, 39.1, 95.0, 95.0, 84.1, 8…
## $ Series_Complete_Janssen <dbl> 303882, 2997, 742926, 165248, 1…
## $ Series_Complete_Moderna <dbl> 1389641, 23275, 3373880, 741446…
## $ Series_Complete_Pfizer <dbl> 2274436, 9586, 5299518, 1123041…
## $ Series_Complete_Novavax <dbl> 597, 0, 882, 227, 1, 221, 136, …
## $ Series_Complete_Unk_Manuf <dbl> 1853, 5, 914, 1157, 3, 2035, 36…
## $ Series_Complete_Janssen_5Plus <dbl> 303866, 2995, 742869, 165246, 1…
## $ Series_Complete_Moderna_5Plus <dbl> 1376509, 22334, 3327352, 736619…
## $ Series_Complete_Pfizer_5Plus <dbl> 2270984, 9573, 5293790, 1121080…
## $ Series_Complete_Unk_Manuf_5Plus <dbl> 1827, 5, 907, 1146, 3, 2034, 36…
## $ Series_Complete_Janssen_12Plus <dbl> 303850, 2993, 742793, 165241, 1…
## $ Series_Complete_Moderna_12Plus <dbl> 1375439, 22299, 3323605, 736006…
## $ Series_Complete_Pfizer_12Plus <dbl> 2106840, 5264, 4928341, 1042891…
## $ Series_Complete_Unk_Manuf_12Plus <dbl> 1723, 5, 899, 1034, 3, 2012, 31…
## $ Series_Complete_Janssen_18Plus <dbl> 303638, 2982, 742204, 165136, 1…
## $ Series_Complete_Moderna_18Plus <dbl> 1374651, 22248, 3319423, 735431…
## $ Series_Complete_Pfizer_18Plus <dbl> 1857807, 775, 4377515, 916134, …
## $ Series_Complete_Unk_Manuf_18Plus <dbl> 1517, 4, 827, 900, 3, 1896, 272…
## $ Series_Complete_Janssen_65Plus <dbl> 30989, 123, 93526, 14716, 120, …
## $ Series_Complete_Moderna_65Plus <dbl> 480190, 1304, 1139942, 278489, …
## $ Series_Complete_Pfizer_65Plus <dbl> 496936, 31, 1209044, 239170, 25…
## $ Series_Complete_Unk_Manuf_65Plus <dbl> 454, 0, 328, 244, 0, 898, 38, 7…
## $ Additional_Doses <dbl> 2429374, 17330, 4486440, 117299…
## $ Additional_Doses_Vax_Pct <dbl> 61.0, 48.3, 47.5, 57.6, 54.3, 4…
## $ Additional_Doses_5Plus <dbl> 2426612, 17269, 4478372, 117230…
## $ Additional_Doses_5Plus_Vax_Pct <dbl> 61.4, 49.5, 47.8, 57.9, 54.5, 4…
## $ Additional_Doses_12Plus <dbl> 2372823, 16828, 4388878, 114933…
## $ Additional_Doses_12Plus_Vax_Pct <dbl> 62.6, 55.1, 48.8, 59.1, 59.0, 4…
## $ Additional_Doses_18Plus <dbl> 2268903, 15432, 4207790, 110280…
## $ Additional_Doses_18Plus_Vax_Pct <dbl> 64.1, 59.3, 49.9, 60.7, 61.9, 5…
## $ Additional_Doses_50Plus <dbl> 1485438, 4369, 2881528, 745903,…
## $ Additional_Doses_50Plus_Vax_Pct <dbl> 75.5, 72.2, 60.9, 73.9, 74.9, 6…
## $ Additional_Doses_65Plus <dbl> 846948, 1098, 1680708, 439523, …
## $ Additional_Doses_65Plus_Vax_Pct <dbl> 84.0, 75.3, 68.8, 82.5, 81.7, 7…
## $ Additional_Doses_Moderna <dbl> 965050, 13715, 1883230, 505586,…
## $ Additional_Doses_Pfizer <dbl> 1435914, 3537, 2538678, 652564,…
## $ Additional_Doses_Janssen <dbl> 27840, 78, 64268, 14391, 217, 2…
## $ Additional_Doses_Unk_Manuf <dbl> 474, 0, 219, 418, 0, 416, 254, …
## $ Second_Booster <dbl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Second_Booster_50Plus <dbl> 939055, 2018, 1443765, 459549, …
## $ Second_Booster_50Plus_Vax_Pct <dbl> 63.2, 46.2, 50.1, 61.6, 26.8, 4…
## $ Second_Booster_65Plus <dbl> 609115, 532, 962775, 308307, 98…
## $ Second_Booster_65Plus_Vax_Pct <dbl> 71.9, 48.5, 57.3, 70.1, 38.3, 5…
## $ Second_Booster_Janssen <dbl> 257, 0, 527, 109, 1, 321, 123, …
## $ Second_Booster_Moderna <dbl> 474352, 3198, 744447, 223548, 5…
## $ Second_Booster_Pfizer <dbl> 754714, 1571, 1059865, 355060, …
## $ Second_Booster_Unk_Manuf <dbl> 148, 0, 69, 240, 0, 175, 58, 5,…
## $ Administered_Bivalent <dbl> 1329420, 5804, 2479433, 633567,…
## $ Admin_Bivalent_PFR <dbl> 874690, 3786, 1614515, 438061, …
## $ Admin_Bivalent_MOD <dbl> 454730, 2018, 864918, 195506, 5…
## $ Dist_Bivalent_PFR <dbl> 1755930, 14700, 4049030, 983130…
## $ Dist_Bivalent_MOD <dbl> 827560, 5600, 1840220, 408980, …
## $ Bivalent_Booster_5Plus <dbl> 1313948, 5681, 2383302, 626574,…
## $ Bivalent_Booster_5Plus_Pop_Pct <dbl> 23.9, 8.2, 19.7, 21.2, 9.9, 13.…
## $ Bivalent_Booster_12Plus <dbl> 1282077, 5176, 2329557, 613751,…
## $ Bivalent_Booster_12Plus_Pop_Pct <dbl> 25.6, 9.0, 21.0, 22.9, 10.6, 14…
## $ Bivalent_Booster_18Plus <dbl> 1235965, 4486, 2252226, 593246,…
## $ Bivalent_Booster_18Plus_Pop_Pct <dbl> 27.1, 9.4, 22.2, 24.4, 11.2, 15…
## $ Bivalent_Booster_65Plus <dbl> 591007, 356, 1095875, 305171, 8…
## $ Bivalent_Booster_65Plus_Pop_Pct <dbl> 58.1, 9.5, 45.8, 55.2, 21.3, 36…
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 6
## isType tot_cases tot_deaths new_cases new_deaths n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 8.43e+9 1.11e+8 1.04e+8 1129008 10089
## 2 after 8.36e+9 1.10e+8 1.03e+8 1122409 8721
## 3 pctchg 8.81e-3 4.95e-3 1.22e-2 0.00584 0.136## Warning: Using `all_of()` outside of a selecting function was deprecated in tidyselect
## 1.2.0.
## ℹ See details at
## <https://tidyselect.r-lib.org/reference/faq-selection-context.html>## Warning: Using `by = character()` to perform a cross join was deprecated in dplyr 1.1.0.
## ℹ Please use `cross_join()` instead.##
## Processed for cdcWeeklyBurden:
## Rows: 60,741
## Columns: 6
## $ date <date> 2020-01-22, 2020-01-23, 2020-01-24, 2020-01-25, 2020-01-26…
## $ state <chr> "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK",…
## $ tot_cases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tot_deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ new_cases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ new_deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 5
## isType inp hosp_adult hosp_ped n
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 before 5.81e+7 5.11e+7 1533306 62162
## 2 after 5.78e+7 5.09e+7 1504781 59268
## 3 pctchg 5.36e-3 5.11e-3 0.0186 0.0466
##
##
## Processed for cdcHosp:
## Rows: 59,268
## Columns: 5
## $ date <date> 2021-02-23, 2021-02-05, 2021-02-02, 2021-01-26, 2021-01-20…
## $ state <chr> "RI", "AK", "KY", "LA", "MD", "SD", "ME", "AL", "MO", "OR",…
## $ inp <dbl> 207, 43, 1365, 1502, 2180, 189, 227, 3340, 2597, 512, 449, …
## $ hosp_adult <dbl> 207, 43, 1350, 1488, 2161, 189, 225, 3309, 2524, 507, 446, …
## $ hosp_ped <dbl> 0, 0, 15, 14, 19, 0, 2, 31, 73, 5, 3, 1, 3, 70, 4, 21, 2, 8…
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 9
## isType vxa vxc vxcpoppct vxcgte65 vxcgt…¹ vxcgte18 vxcgt…² n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 4.59e+11 1.86e+11 1653142. 4.67e+10 2.42e+6 1.71e+11 1.94e+6 3.84e+4
## 2 after 2.21e+11 8.98e+10 1382689. 2.26e+10 2.14e+6 8.29e+10 1.64e+6 3.03e+4
## 3 pctchg 5.18e- 1 5.16e- 1 0.164 5.16e- 1 1.16e-1 5.16e- 1 1.54e-1 2.09e-1
## # … with abbreviated variable names ¹vxcgte65pct, ²vxcgte18pct
##
##
## Processed for vax:
## Rows: 44,064
## Columns: 9
## $ date <date> 2020-12-14, 2020-12-15, 2020-12-16, 2020-12-17, 2020-12-1…
## $ state <chr> "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK"…
## $ vxa <dbl> 0, 0, 0, 2, 2, 1607, 4239, 5125, 5615, 6822, 8578, 10612, …
## $ vxc <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ vxcpoppct <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ vxcgte65 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ vxcgte65pct <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ vxcgte18 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ vxcgte18pct <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
##
## Integrated per capita data file:
## Rows: 61,158
## Columns: 34
## $ date <date> 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-0…
## $ state <chr> "AL", "HI", "IN", "LA", "MN", "MT", "NC", "TX", "AL", "HI"…
## $ tot_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tot_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ new_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ new_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ inp <dbl> NA, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 1877, 0, …
## $ hosp_adult <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hosp_ped <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxa <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxc <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpoppct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte65 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte65pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte18 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte18pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tdpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ cpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ dpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hpm <dbl> NA, 0.0000, 0.0000, NA, 0.0000, 0.0000, 0.0000, 0.0000, NA…
## $ ahpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ phpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxapm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tdpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ cpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ dpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ ahpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ phpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxapm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.## Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
saveToRDS(cdc_daily_230502, ovrWriteError=FALSE)The latest hospitalization data is also downloaded and processed:
# Run for latest data, save as RDS
indivHosp_20230503 <- downloadReadHospitalData(loc="./RInputFiles/Coronavirus/HHS_Hospital_20230503.csv")##
## File ./RInputFiles/Coronavirus/HHS_Hospital_20230503.csv already exists
## File will not be downloaded since ovrWrite is not TRUE## Rows: 285856 Columns: 128
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (11): hospital_pk, state, ccn, hospital_name, address, city, zip, hosp...
## dbl (114): total_beds_7_day_avg, all_adult_hospital_beds_7_day_avg, all_adu...
## lgl (2): is_metro_micro, is_corrected
## date (1): collection_week
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.## Rows: 285,856
## Columns: 128
## $ hospital_pk <chr> …
## $ collection_week <date> …
## $ state <chr> …
## $ ccn <chr> …
## $ hospital_name <chr> …
## $ address <chr> …
## $ city <chr> …
## $ zip <chr> …
## $ hospital_subtype <chr> …
## $ fips_code <chr> …
## $ is_metro_micro <lgl> …
## $ total_beds_7_day_avg <dbl> …
## $ all_adult_hospital_beds_7_day_avg <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_avg <dbl> …
## $ inpatient_beds_used_7_day_avg <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_avg <dbl> …
## $ inpatient_beds_used_covid_7_day_avg <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_avg <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_avg <dbl> …
## $ inpatient_beds_7_day_avg <dbl> …
## $ total_icu_beds_7_day_avg <dbl> …
## $ total_staffed_adult_icu_beds_7_day_avg <dbl> …
## $ icu_beds_used_7_day_avg <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_avg <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_avg <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_avg <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_avg <dbl> …
## $ icu_patients_confirmed_influenza_7_day_avg <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_avg <dbl> …
## $ total_beds_7_day_sum <dbl> …
## $ all_adult_hospital_beds_7_day_sum <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_sum <dbl> …
## $ inpatient_beds_used_7_day_sum <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_sum <dbl> …
## $ inpatient_beds_used_covid_7_day_sum <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_sum <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_sum <dbl> …
## $ inpatient_beds_7_day_sum <dbl> …
## $ total_icu_beds_7_day_sum <dbl> …
## $ total_staffed_adult_icu_beds_7_day_sum <dbl> …
## $ icu_beds_used_7_day_sum <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_sum <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_sum <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_sum <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_sum <dbl> …
## $ icu_patients_confirmed_influenza_7_day_sum <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_sum <dbl> …
## $ total_beds_7_day_coverage <dbl> …
## $ all_adult_hospital_beds_7_day_coverage <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_coverage <dbl> …
## $ inpatient_beds_used_7_day_coverage <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_coverage <dbl> …
## $ inpatient_beds_used_covid_7_day_coverage <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_coverage <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_coverage <dbl> …
## $ inpatient_beds_7_day_coverage <dbl> …
## $ total_icu_beds_7_day_coverage <dbl> …
## $ total_staffed_adult_icu_beds_7_day_coverage <dbl> …
## $ icu_beds_used_7_day_coverage <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_coverage <dbl> …
## $ icu_patients_confirmed_influenza_7_day_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_coverage <dbl> …
## $ previous_day_admission_adult_covid_confirmed_7_day_sum <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+_7_day_sum` <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_7_day_sum <dbl> …
## $ previous_day_covid_ED_visits_7_day_sum <dbl> …
## $ previous_day_admission_adult_covid_suspected_7_day_sum <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+_7_day_sum` <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_7_day_sum <dbl> …
## $ previous_day_total_ED_visits_7_day_sum <dbl> …
## $ previous_day_admission_influenza_confirmed_7_day_sum <dbl> …
## $ geocoded_hospital_address <chr> …
## $ hhs_ids <chr> …
## $ previous_day_admission_adult_covid_confirmed_7_day_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_7_day_coverage <dbl> …
## $ previous_day_admission_adult_covid_suspected_7_day_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_7_day_coverage <dbl> …
## $ previous_week_personnel_covid_vaccinated_doses_administered_7_day <dbl> …
## $ total_personnel_covid_vaccinated_doses_none_7_day <dbl> …
## $ total_personnel_covid_vaccinated_doses_one_7_day <dbl> …
## $ total_personnel_covid_vaccinated_doses_all_7_day <dbl> …
## $ previous_week_patients_covid_vaccinated_doses_one_7_day <dbl> …
## $ previous_week_patients_covid_vaccinated_doses_all_7_day <dbl> …
## $ is_corrected <lgl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_avg <dbl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_coverage <dbl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_sum <dbl> …
## $ all_pediatric_inpatient_beds_7_day_avg <dbl> …
## $ all_pediatric_inpatient_beds_7_day_coverage <dbl> …
## $ all_pediatric_inpatient_beds_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown_7_day_sum <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_avg <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_coverage <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_sum <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_avg <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_coverage <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_sum <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_avg <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_coverage <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_sum <dbl> …
##
## Hospital Subtype Counts:
## # A tibble: 4 × 2
## hospital_subtype n
## <chr> <int>
## 1 Childrens Hospitals 5313
## 2 Critical Access Hospitals 76897
## 3 Long Term 19259
## 4 Short Term 184387
##
## Records other than 50 states and DC
## # A tibble: 5 × 2
## state n
## <chr> <int>
## 1 AS 32
## 2 GU 116
## 3 MP 39
## 4 PR 2737
## 5 VI 110
##
## Record types for key metrics## Warning: Specifying the `id_cols` argument by position was deprecated in tidyr 1.3.0.
## ℹ Please explicitly name `id_cols`, like `id_cols = name`.## # A tibble: 10 × 5
## name `NA` Posit…¹ Value…² Total
## <chr> <int> <int> <int> <int>
## 1 all_adult_hospital_beds_7_day_avg 87037 198399 420 285856
## 2 all_adult_hospital_inpatient_bed_occupied_7_day… 31637 232980 21239 285856
## 3 icu_beds_used_7_day_avg 34854 220054 30948 285856
## 4 inpatient_beds_7_day_avg 8673 276090 1093 285856
## 5 inpatient_beds_used_7_day_avg 8673 254066 23117 285856
## 6 inpatient_beds_used_covid_7_day_avg 1778 189444 94634 285856
## 7 staffed_icu_adult_patients_confirmed_and_suspec… 31599 172182 82075 285856
## 8 total_adult_patients_hospitalized_confirmed_and… 28937 171663 85256 285856
## 9 total_beds_7_day_avg 61699 223911 246 285856
## 10 total_icu_beds_7_day_avg 3437 267929 14490 285856
## # … with abbreviated variable names ¹Positive, ²`Value -999999`## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
saveToRDS(indivHosp_20230503, ovrWriteError=FALSE)Post-processing is run, including hospital summaries:
# Create pivoted burden data
burdenPivotList_230502 <- postProcessCDCDaily(cdc_daily_230502,
dataThruLabel="Apr 2023",
keyDatesBurden=c("2023-04-26", "2022-06-30",
"2021-12-31", "2021-06-30"
),
keyDatesVaccine=c("2023-04-26", "2021-12-31",
"2021-08-31", "2021-03-31"
),
returnData=TRUE
)## Joining with `by = join_by(state)`##
## *** File has been checked for uniqueness by: state date name## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.## Warning: Removed 24 rows containing missing values (`geom_line()`).
## Warning: Removed 24 rows containing missing values (`position_stack()`).
## Warning: Removed 24 rows containing missing values (`position_stack()`).
## Warning: Removed 9 rows containing missing values (`geom_line()`).## Warning: There was 1 warning in `filter()`.
## ℹ In argument: `date %in% all_of(keyDates)`.
## Caused by warning:
## ! Using `all_of()` outside of a selecting function was deprecated in tidyselect
## 1.2.0.
## ℹ See details at
## <https://tidyselect.r-lib.org/reference/faq-selection-context.html>## Warning: Specifying the `id_cols` argument by position was deprecated in tidyr 1.3.0.
## ℹ Please explicitly name `id_cols`, like `id_cols = state`.## Warning: Specifying the `id_cols` argument by position was deprecated in tidyr 1.3.0.
## ℹ Please explicitly name `id_cols`, like `id_cols = state`.
## Warning: Specifying the `id_cols` argument by position was deprecated in tidyr 1.3.0.
## ℹ Please explicitly name `id_cols`, like `id_cols = state`.## Warning: Specifying the `id_cols` argument by position was deprecated in tidyr 1.3.0.
## ℹ Please explicitly name `id_cols`, like `id_cols = state`.
## Warning: Specifying the `id_cols` argument by position was deprecated in tidyr 1.3.0.
## ℹ Please explicitly name `id_cols`, like `id_cols = state`.## Warning: Specifying the `id_cols` argument by position was deprecated in tidyr 1.3.0.
## ℹ Please explicitly name `id_cols`, like `id_cols = state`.
# Create hospitalized per capita data
hospPerCap_230502 <- hospAgePerCapita(readFromRDS("dfStateAgeBucket2019"),
lst=burdenPivotList_230502,
popVar="pop2019",
excludeState=c(),
cumStartDate="2020-07-15"
)## Warning: Removed 18 rows containing missing values (`geom_line()`).
burdenPivotList_230502$hospAge %>%
group_by(adultPed, confSusp, age, name) %>%
summarize(value=sum(value, na.rm=TRUE), n=n(), .groups="drop")## # A tibble: 18 × 6
## adultPed confSusp age name value n
## <chr> <chr> <chr> <chr> <dbl> <int>
## 1 adult confirmed 0-19 previous_day_admission_adult_covid_con… 5.15e4 62162
## 2 adult confirmed 20-29 previous_day_admission_adult_covid_con… 3.22e5 62162
## 3 adult confirmed 30-39 previous_day_admission_adult_covid_con… 4.61e5 62162
## 4 adult confirmed 40-49 previous_day_admission_adult_covid_con… 5.42e5 62162
## 5 adult confirmed 50-59 previous_day_admission_adult_covid_con… 8.73e5 62162
## 6 adult confirmed 60-69 previous_day_admission_adult_covid_con… 1.19e6 62162
## 7 adult confirmed 70-79 previous_day_admission_adult_covid_con… 1.24e6 62162
## 8 adult confirmed 80+ previous_day_admission_adult_covid_con… 1.19e6 62162
## 9 adult suspected 0-19 previous_day_admission_adult_covid_sus… 4.54e4 62162
## 10 adult suspected 20-29 previous_day_admission_adult_covid_sus… 3.02e5 62162
## 11 adult suspected 30-39 previous_day_admission_adult_covid_sus… 3.98e5 62162
## 12 adult suspected 40-49 previous_day_admission_adult_covid_sus… 4.04e5 62162
## 13 adult suspected 50-59 previous_day_admission_adult_covid_sus… 6.42e5 62162
## 14 adult suspected 60-69 previous_day_admission_adult_covid_sus… 9.03e5 62162
## 15 adult suspected 70-79 previous_day_admission_adult_covid_sus… 8.90e5 62162
## 16 adult suspected 80+ previous_day_admission_adult_covid_sus… 8.19e5 62162
## 17 ped confirmed 0-19 previous_day_admission_pediatric_covid… 2.05e5 62162
## 18 ped suspected 0-19 previous_day_admission_pediatric_covid… 4.84e5 62162saveToRDS(burdenPivotList_230502, ovrWriteError=FALSE)
saveToRDS(hospPerCap_230502, ovrWriteError=FALSE)Peaks and valleys of key metrics are also updated:
peakValleyCDCDaily(cdc_daily_230502)## Warning: Using `all_of()` outside of a selecting function was deprecated in tidyselect
## 1.2.0.
## ℹ See details at
## <https://tidyselect.r-lib.org/reference/faq-selection-context.html>## Warning: There was 1 warning in `summarize()`.
## ℹ In argument: `across(all_of(numVar), .fns = sum, na.rm = TRUE)`.
## ℹ In group 1: `date = 2020-01-01`, `regn = "North Central"`.
## Caused by warning:
## ! The `...` argument of `across()` is deprecated as of dplyr 1.1.0.
## Supply arguments directly to `.fns` through an anonymous function instead.
##
## # Previously
## across(a:b, mean, na.rm = TRUE)
##
## # Now
## across(a:b, \(x) mean(x, na.rm = TRUE))## Warning: Removed 6 rows containing missing values (`geom_line()`).
## Warning: Removed 6 rows containing missing values (`geom_line()`).
## Warning: Removed 6 rows containing missing values (`geom_line()`).
## Warning: Removed 20 rows containing missing values (`geom_line()`).
## Warning: Removed 20 rows containing missing values (`geom_line()`).
## # A tibble: 10,584 × 8
## date state vxa vxc vxa_isPeak vxc_isPeak vxa_isValley vxc_isValley
## <date> <chr> <dbl> <dbl> <lgl> <lgl> <lgl> <lgl>
## 1 2020-12-01 CA NA NA FALSE FALSE FALSE FALSE
## 2 2020-12-01 FL NA NA FALSE FALSE FALSE FALSE
## 3 2020-12-01 GA NA NA FALSE FALSE FALSE FALSE
## 4 2020-12-01 IL NA NA FALSE FALSE FALSE FALSE
## 5 2020-12-01 MI NA NA FALSE FALSE FALSE FALSE
## 6 2020-12-01 NC NA NA FALSE FALSE FALSE FALSE
## 7 2020-12-01 NJ NA NA FALSE FALSE FALSE FALSE
## 8 2020-12-01 NY NA NA FALSE FALSE FALSE FALSE
## 9 2020-12-01 OH NA NA FALSE FALSE FALSE FALSE
## 10 2020-12-01 PA NA NA FALSE FALSE FALSE FALSE
## # … with 10,574 more rowsHospital data are pieced together as needed:
# Create modified hospital data
multiSourceHosp_20230502 <- multiSourceDataCombine(list(readFromRDS("indivHosp_20220704"),
readFromRDS("indivHosp_20230503")
),
timeVec=as.Date("2022-01-01")
)The updated hospital data are then plotted:
# Run hospital plots
modStateHosp_20230502 <- hospitalCapacityCDCDaily(multiSourceHosp_20230502,
plotSub="Aug 2020 to Apr 2023\nOld data used pre-2022"
)## Warning: Using `all_of()` outside of a selecting function was deprecated in tidyselect
## 1.2.0.
## ℹ See details at
## <https://tidyselect.r-lib.org/reference/faq-selection-context.html>## Warning: There was 1 warning in `summarize()`.
## ℹ In argument: `across(where(is.numeric), .fns = sum, na.rm = TRUE)`.
## ℹ In group 1: `state = "AK"`, `collection_week = 2020-07-31`.
## Caused by warning:
## ! The `...` argument of `across()` is deprecated as of dplyr 1.1.0.
## Supply arguments directly to `.fns` through an anonymous function instead.
##
## # Previously
## across(a:b, mean, na.rm = TRUE)
##
## # Now
## across(a:b, \(x) mean(x, na.rm = TRUE))## Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
## ℹ Please use tidy evaluation ideoms with `aes()`
Data From 2023-06-02
The latest data are downloaded and processed:
readList <- list("cdcWeeklyBurden"="./RInputFiles/Coronavirus/CDC_dc_wkly_downloaded_230602.csv",
"cdcHosp"="./RInputFiles/Coronavirus/CDC_h_downloaded_230602.csv",
"vax"="./RInputFiles/Coronavirus/vaxData_downloaded_230602.csv"
)
compareList <- list("cdcWeeklyBurden"=readFromRDS("cdc_daily_230502")$dfRaw$cdcWeeklyBurden,
"cdcHosp"=readFromRDS("cdc_daily_230502")$dfRaw$cdcHosp,
"vax"=readFromRDS("cdc_daily_230502")$dfRaw$vax
)
cdc_daily_230602 <- readRunCDCDaily(thruLabel="May 31, 2023",
downloadTo=lapply(readList, FUN=function(x) if(file.exists(x)) NA else x),
readFrom=readList,
compareFile=compareList,
writeLog=NULL,
useClusters=readFromRDS("cdc_daily_210528")$useClusters,
weightedMeanAggs=c("tcpm7", "tdpm7", "cpm7", "dpm7", "hpm7",
"vxcpm7", "vxcgte65pct"
),
skipAssessmentPlots=FALSE,
brewPalette="Paired"
)## Rows: 10380 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): date_updated, state, start_date, end_date
## dbl (6): tot_cases, new_cases, tot_deaths, new_deaths, new_historic_cases, n...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.##
## *** File has been checked for uniqueness by: state date## Warning: There was 1 warning in `summarize()`.
## ℹ In argument: `across(.cols = all_of(useVars), .fns = fn, ...)`.
## ℹ In group 1: `date = 2020-01-22`.
## Caused by warning:
## ! The `...` argument of `across()` is deprecated as of dplyr 1.1.0.
## Supply arguments directly to `.fns` through an anonymous function instead.
##
## # Previously
## across(a:b, mean, na.rm = TRUE)
##
## # Now
## across(a:b, \(x) mean(x, na.rm = TRUE))## Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
## ℹ Please use tidy evaluation ideoms with `aes()`
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 2
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2023-04-26 new_cases 96470 88330 8140 0.08809524
## 2 2023-04-19 new_cases 106903 97893 9010 0.08799000
## 3 2023-04-12 new_cases 110152 101854 8298 0.07828080
## 4 2023-04-05 new_cases 131959 123107 8852 0.06940949
## 5 2023-03-29 new_cases 145340 137741 7599 0.05368781## Warning in left_join(., df, by = names(univData)): Each row in `x` is expected to match at most 1 row in `y`.
## ℹ Row 1 of `x` matches multiple rows.
## ℹ If multiple matches are expected, set `multiple = "all"` to silence this
## warning.## Warning in left_join(., ref, by = names(univData)): Each row in `x` is expected to match at most 1 row in `y`.
## ℹ Row 1 of `x` matches multiple rows.
## ℹ If multiple matches are expected, set `multiple = "all"` to silence this
## warning.
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 DE tot_deaths 334319 333766 553 0.001655478
## 2 CO tot_cases 144279071 143068917 1210154 0.008422916
## 3 RI tot_cases 35753640 35495942 257698 0.007233671
## 4 MO tot_cases 148871461 148305521 565940 0.003808774
## 5 FL tot_cases 615401561 616540353 1138792 0.001848775
## 6 DE tot_cases 26963948 26921266 42682 0.001584182
## 7 DE new_deaths 3394 3386 8 0.002359882
## 8 VT new_deaths 962 960 2 0.002081165
## 9 TN new_deaths 29522 29468 54 0.001830819
## 10 IL new_deaths 41926 41970 44 0.001048918
## 11 PA new_deaths 50999 50946 53 0.001039776
## 12 FL new_cases 7558324 7526876 31448 0.004169385
## 13 KY new_cases 1740761 1738773 1988 0.001142682
##
##
##
## Raw file for cdcWeeklyBurden:
## Rows: 10,380
## Columns: 10
## $ date_updated <chr> "01/23/2020", "01/30/2020", "02/06/2020", "02/13/2…
## $ state <chr> "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", "A…
## $ start_date <chr> "01/16/2020", "01/23/2020", "01/30/2020", "02/06/2…
## $ date <date> 2020-01-22, 2020-01-29, 2020-02-05, 2020-02-12, 2…
## $ tot_cases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 11, 63, 149, 235, 300, 337…
## $ new_cases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 11, 52, 86, 86, 65, 37, 18…
## $ tot_deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 7, 9, 9, 9, 10, 1…
## $ new_deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 4, 2, 0, 0, 1, 0,…
## $ new_historic_cases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ new_historic_deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…## Rows: 63839 Columns: 135
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): state
## dbl (132): critical_staffing_shortage_today_yes, critical_staffing_shortage...
## lgl (1): geocoded_state
## date (1): date
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 31
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2023-05-01 inp 13811 13133 678 0.05032660
## 2 2023-05-01 hosp_ped 663 616 47 0.07349492## Warning in left_join(., df, by = names(univData)): Each row in `x` is expected to match at most 1 row in `y`.
## Each row in `x` is expected to match at most 1 row in `y`.
## ℹ Row 1 of `x` matches multiple rows.
## ℹ If multiple matches are expected, set `multiple = "all"` to silence this
## warning.
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 AK inp 72484 72712 228 0.003140582
## 2 IN inp 1239470 1237115 2355 0.001901812
## 3 AK hosp_ped 3274 3299 25 0.007606877
## 4 OR hosp_ped 16993 17018 25 0.001470113
## 5 WV hosp_ped 7430 7439 9 0.001210572
## 6 HI hosp_ped 4703 4708 5 0.001062586
## 7 MT hosp_ped 3941 3945 4 0.001014456
## 8 AK hosp_adult 67115 67318 203 0.003020092
## 9 IN hosp_adult 1115736 1113336 2400 0.002153362
## 10 VI hosp_adult 5893 5887 6 0.001018676
##
##
##
## Raw file for cdcHosp:
## Rows: 63,839
## Columns: 135
## $ state <chr> …
## $ date <date> …
## $ critical_staffing_shortage_today_yes <dbl> …
## $ critical_staffing_shortage_today_no <dbl> …
## $ critical_staffing_shortage_today_not_reported <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_yes <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_no <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_not_reported <dbl> …
## $ hospital_onset_covid <dbl> …
## $ hospital_onset_covid_coverage <dbl> …
## $ inpatient_beds <dbl> …
## $ inpatient_beds_coverage <dbl> …
## $ inpatient_beds_used <dbl> …
## $ inpatient_beds_used_coverage <dbl> …
## $ inp <dbl> …
## $ inpatient_beds_used_covid_coverage <dbl> …
## $ previous_day_admission_adult_covid_confirmed <dbl> …
## $ previous_day_admission_adult_covid_confirmed_coverage <dbl> …
## $ previous_day_admission_adult_covid_suspected <dbl> …
## $ previous_day_admission_adult_covid_suspected_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_suspected <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_coverage <dbl> …
## $ staffed_adult_icu_bed_occupancy <dbl> …
## $ staffed_adult_icu_bed_occupancy_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_coverage <dbl> …
## $ hosp_adult <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_coverage <dbl> …
## $ hosp_ped <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_coverage <dbl> …
## $ total_staffed_adult_icu_beds <dbl> …
## $ total_staffed_adult_icu_beds_coverage <dbl> …
## $ inpatient_beds_utilization <dbl> …
## $ inpatient_beds_utilization_coverage <dbl> …
## $ inpatient_beds_utilization_numerator <dbl> …
## $ inpatient_beds_utilization_denominator <dbl> …
## $ percent_of_inpatients_with_covid <dbl> …
## $ percent_of_inpatients_with_covid_coverage <dbl> …
## $ percent_of_inpatients_with_covid_numerator <dbl> …
## $ percent_of_inpatients_with_covid_denominator <dbl> …
## $ inpatient_bed_covid_utilization <dbl> …
## $ inpatient_bed_covid_utilization_coverage <dbl> …
## $ inpatient_bed_covid_utilization_numerator <dbl> …
## $ inpatient_bed_covid_utilization_denominator <dbl> …
## $ adult_icu_bed_covid_utilization <dbl> …
## $ adult_icu_bed_covid_utilization_coverage <dbl> …
## $ adult_icu_bed_covid_utilization_numerator <dbl> …
## $ adult_icu_bed_covid_utilization_denominator <dbl> …
## $ adult_icu_bed_utilization <dbl> …
## $ adult_icu_bed_utilization_coverage <dbl> …
## $ adult_icu_bed_utilization_numerator <dbl> …
## $ adult_icu_bed_utilization_denominator <dbl> …
## $ geocoded_state <lgl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+_coverage` <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown_coverage <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+_coverage` <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown_coverage <dbl> …
## $ deaths_covid <dbl> …
## $ deaths_covid_coverage <dbl> …
## $ on_hand_supply_therapeutic_a_casirivimab_imdevimab_courses <dbl> …
## $ on_hand_supply_therapeutic_b_bamlanivimab_courses <dbl> …
## $ on_hand_supply_therapeutic_c_bamlanivimab_etesevimab_courses <dbl> …
## $ previous_week_therapeutic_a_casirivimab_imdevimab_courses_used <dbl> …
## $ previous_week_therapeutic_b_bamlanivimab_courses_used <dbl> …
## $ previous_week_therapeutic_c_bamlanivimab_etesevimab_courses_used <dbl> …
## $ icu_patients_confirmed_influenza <dbl> …
## $ icu_patients_confirmed_influenza_coverage <dbl> …
## $ previous_day_admission_influenza_confirmed <dbl> …
## $ previous_day_admission_influenza_confirmed_coverage <dbl> …
## $ previous_day_deaths_covid_and_influenza <dbl> …
## $ previous_day_deaths_covid_and_influenza_coverage <dbl> …
## $ previous_day_deaths_influenza <dbl> …
## $ previous_day_deaths_influenza_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_coverage <dbl> …
## $ all_pediatric_inpatient_bed_occupied <dbl> …
## $ all_pediatric_inpatient_bed_occupied_coverage <dbl> …
## $ all_pediatric_inpatient_beds <dbl> …
## $ all_pediatric_inpatient_beds_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4 <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17 <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11 <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown_coverage <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_coverage <dbl> …
## $ staffed_pediatric_icu_bed_occupancy <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_coverage <dbl> …
## $ total_staffed_pediatric_icu_beds <dbl> …
## $ total_staffed_pediatric_icu_beds_coverage <dbl> …## Rows: 38488 Columns: 109
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Date, Location
## dbl (107): MMWR_week, Distributed, Distributed_Janssen, Distributed_Moderna,...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 2
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 1 and at least 1%
##
## [1] date name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)## Warning in left_join(., df, by = names(univData)): Each row in `x` is expected to match at most 1 row in `y`.
## Each row in `x` is expected to match at most 1 row in `y`.
## ℹ Row 1 of `x` matches multiple rows.
## ℹ If multiple matches are expected, set `multiple = "all"` to silence this
## warning.
##
##
## ***Differences of at least 0 and at least 0.1%
##
## [1] state name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
##
##
##
## Raw file for vax:
## Rows: 38,488
## Columns: 109
## $ date <date> 2023-05-10, 2023-05-10, 2023-0…
## $ MMWR_week <dbl> 19, 19, 19, 19, 19, 19, 19, 19,…
## $ state <chr> "NE", "LA", "GA", "WY", "CO", "…
## $ Distributed <dbl> 5481710, 10282120, 28727475, 12…
## $ Distributed_Janssen <dbl> 152400, 330500, 869100, 49300, …
## $ Distributed_Moderna <dbl> 1647380, 3807980, 9763000, 4900…
## $ Distributed_Pfizer <dbl> 2905630, 5164550, 14773655, 585…
## $ Distributed_Novavax <dbl> 7400, 10100, 43400, 3700, 43600…
## $ Distributed_Unk_Manuf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ Dist_Per_100K <dbl> 283379, 221178, 270569, 221466,…
## $ Distributed_Per_100k_5Plus <dbl> 303944, 236516, 288404, 235691,…
## $ Distributed_Per_100k_12Plus <dbl> 338919, 262077, 319617, 261198,…
## $ Distributed_Per_100k_18Plus <dbl> 375889, 288729, 354068, 288019,…
## $ Distributed_Per_100k_65Plus <dbl> 1754380, 1387560, 1893760, 1292…
## $ vxa <dbl> 3822190, 6961453, 17124791, 854…
## $ Administered_5Plus <dbl> 3793971, 6945414, 17045184, 851…
## $ Administered_12Plus <dbl> 3647301, 6796682, 16545894, 831…
## $ Administered_18Plus <dbl> 3412154, 6443990, 15542310, 790…
## $ Administered_65Plus <dbl> 1117112, 2090638, 4409764, 2846…
## $ Administered_Janssen <dbl> 96449, 202173, 349386, 29205, 3…
## $ Administered_Moderna <dbl> 1240872, 2685630, 6170654, 3475…
## $ Administered_Pfizer <dbl> 2124748, 3705194, 9413636, 4106…
## $ Administered_Novavax <dbl> 515, 719, 2451, 204, 2980, 2443…
## $ Administered_Unk_Manuf <dbl> 10726, 3970, 57522, 1271, 21716…
## $ Admin_Per_100k <dbl> 197590, 149748, 161290, 147580,…
## $ Admin_Per_100k_5Plus <dbl> 210364, 159763, 171122, 156569,…
## $ Admin_Per_100k_12Plus <dbl> 225503, 173238, 184087, 169491,…
## $ Admin_Per_100k_18Plus <dbl> 233976, 180952, 191560, 177691,…
## $ Admin_Per_100k_65Plus <dbl> 357524, 282129, 290699, 286978,…
## $ Recip_Administered <dbl> 3839138, 6941996, 17292313, 864…
## $ Administered_Dose1_Recip <dbl> 1425923, 2924163, 7287758, 3537…
## $ Administered_Dose1_Pop_Pct <dbl> 73.7, 62.9, 68.6, 61.1, 84.0, 9…
## $ Administered_Dose1_Recip_5Plus <dbl> 1412159, 2915981, 7248684, 3523…
## $ Administered_Dose1_Recip_5PlusPop_Pct <dbl> 78.3, 67.1, 72.8, 64.8, 88.0, 9…
## $ Administered_Dose1_Recip_12Plus <dbl> 1345134, 2833936, 6998957, 3424…
## $ Administered_Dose1_Recip_12PlusPop_Pct <dbl> 83.2, 72.2, 77.9, 69.8, 92.2, 9…
## $ Administered_Dose1_Recip_18Plus <dbl> 1244223, 2656169, 6507679, 3229…
## $ Administered_Dose1_Recip_18PlusPop_Pct <dbl> 85.3, 74.6, 80.2, 72.6, 93.8, 9…
## $ Administered_Dose1_Recip_65Plus <dbl> 318755, 687335, 1494072, 97375,…
## $ Administered_Dose1_Recip_65PlusPop_Pct <dbl> 95.0, 92.8, 95.0, 95.0, 95.0, 9…
## $ vxc <dbl> 1286860, 2561641, 6103647, 3076…
## $ vxcpoppct <dbl> 66.5, 55.1, 57.5, 53.2, 73.8, 7…
## $ Series_Complete_5Plus <dbl> 1278092, 2558038, 6085428, 3069…
## $ Series_Complete_5PlusPop_Pct <dbl> 70.9, 58.8, 61.1, 56.4, 77.7, 7…
## $ Series_Complete_12Plus <dbl> 1219736, 2497575, 5894565, 2987…
## $ Series_Complete_12PlusPop_Pct <dbl> 75.4, 63.7, 65.6, 60.9, 81.5, 8…
## $ vxcgte18 <dbl> 1128474, 2350247, 5494615, 2819…
## $ vxcgte18pct <dbl> 77.4, 66.0, 67.7, 63.3, 82.9, 8…
## $ vxcgte65 <dbl> 297818, 647350, 1315992, 87140,…
## $ vxcgte65pct <dbl> 95.0, 87.4, 86.8, 87.9, 95.0, 9…
## $ Series_Complete_Janssen <dbl> 90057, 183620, 329574, 27160, 3…
## $ Series_Complete_Moderna <dbl> 433872, 984393, 2227274, 124833…
## $ Series_Complete_Pfizer <dbl> 758745, 1390083, 3520592, 15490…
## $ Series_Complete_Novavax <dbl> 186, 244, 853, 70, 974, 907, 73…
## $ Series_Complete_Unk_Manuf <dbl> 2568, 1599, 17689, 375, 5912, 9…
## $ Series_Complete_Janssen_5Plus <dbl> 90040, 183598, 329466, 27157, 3…
## $ Series_Complete_Moderna_5Plus <dbl> 427369, 982648, 2220174, 124437…
## $ Series_Complete_Pfizer_5Plus <dbl> 757950, 1389956, 3517415, 15489…
## $ Series_Complete_Unk_Manuf_5Plus <dbl> 2547, 1593, 17530, 373, 5839, 9…
## $ Series_Complete_Janssen_12Plus <dbl> 90027, 183582, 329420, 27156, 3…
## $ Series_Complete_Moderna_12Plus <dbl> 427057, 982374, 2219163, 124395…
## $ Series_Complete_Pfizer_12Plus <dbl> 700015, 1329829, 3328796, 14677…
## $ Series_Complete_Unk_Manuf_12Plus <dbl> 2451, 1547, 16346, 346, 5522, 9…
## $ Series_Complete_Janssen_18Plus <dbl> 89943, 183374, 329010, 27135, 3…
## $ Series_Complete_Moderna_18Plus <dbl> 426673, 981633, 2217105, 124344…
## $ Series_Complete_Pfizer_18Plus <dbl> 609469, 1183540, 2933657, 13002…
## $ Series_Complete_Unk_Manuf_18Plus <dbl> 2215, 1470, 14037, 328, 4639, 8…
## $ Series_Complete_Janssen_65Plus <dbl> 7102, 22545, 35851, 4009, 29304…
## $ Series_Complete_Moderna_65Plus <dbl> 143236, 308249, 713682, 44799, …
## $ Series_Complete_Pfizer_65Plus <dbl> 146396, 316290, 562612, 38214, …
## $ Series_Complete_Unk_Manuf_65Plus <dbl> 1065, 238, 3731, 107, 1310, 330…
## $ Additional_Doses <dbl> 718168, 1110217, 2705136, 14560…
## $ Additional_Doses_Vax_Pct <dbl> 55.8, 43.3, 44.3, 47.3, 57.9, 4…
## $ Additional_Doses_5Plus <dbl> 716720, 1110101, 2703444, 14557…
## $ Additional_Doses_5Plus_Vax_Pct <dbl> 56.1, 43.4, 44.4, 47.4, 58.3, 4…
## $ Additional_Doses_12Plus <dbl> 699462, 1103756, 2666367, 14398…
## $ Additional_Doses_12Plus_Vax_Pct <dbl> 57.3, 44.2, 45.2, 48.2, 59.4, 4…
## $ Additional_Doses_18Plus <dbl> 664415, 1076600, 2567417, 13959…
## $ Additional_Doses_18Plus_Vax_Pct <dbl> 58.9, 45.8, 46.7, 49.5, 60.8, 4…
## $ Additional_Doses_50Plus <dbl> 418372, 767582, 1667644, 98909,…
## $ Additional_Doses_50Plus_Vax_Pct <dbl> 72.2, 59.5, 59.0, 61.8, 73.8, 6…
## $ Additional_Doses_65Plus <dbl> 240367, 450792, 892658, 62044, …
## $ Additional_Doses_65Plus_Vax_Pct <dbl> 80.7, 69.6, 67.8, 71.2, 81.0, 6…
## $ Additional_Doses_Moderna <dbl> 265916, 486814, 1155986, 66344,…
## $ Additional_Doses_Pfizer <dbl> 443827, 608975, 1518137, 77096,…
## $ Additional_Doses_Janssen <dbl> 7164, 14004, 26699, 1930, 27303…
## $ Additional_Doses_Unk_Manuf <dbl> 1215, 364, 4168, 214, 2547, 222…
## $ Second_Booster <dbl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Second_Booster_50Plus <dbl> 245727, 320045, 747349, 48966, …
## $ Second_Booster_50Plus_Vax_Pct <dbl> 58.7, 41.7, 44.8, 49.5, 62.9, 5…
## $ Second_Booster_65Plus <dbl> 159724, 216649, 459153, 34681, …
## $ Second_Booster_65Plus_Vax_Pct <dbl> 66.5, 48.1, 51.4, 55.9, 70.8, 5…
## $ Second_Booster_Janssen <dbl> 131, 180, 481, 28, 471, 528, 92…
## $ Second_Booster_Moderna <dbl> 107459, 160184, 411155, 25485, …
## $ Second_Booster_Pfizer <dbl> 217348, 215901, 541245, 33167, …
## $ Second_Booster_Unk_Manuf <dbl> 692, 84, 1166, 98, 1266, 71, 32…
## $ Administered_Bivalent <dbl> 348027, 363519, 1128249, 65185,…
## $ Admin_Bivalent_PFR <dbl> 253678, 223481, 707301, 40288, …
## $ Admin_Bivalent_MOD <dbl> 94349, 140038, 420948, 24897, 3…
## $ Dist_Bivalent_PFR <dbl> 575480, 640590, 2255000, 102510…
## $ Dist_Bivalent_MOD <dbl> 193420, 328400, 1023320, 50600,…
## $ Bivalent_Booster_5Plus <dbl> 340508, 359506, 1126791, 65920,…
## $ Bivalent_Booster_5Plus_Pop_Pct <dbl> 18.9, 8.3, 11.3, 12.1, 23.4, 19…
## $ Bivalent_Booster_12Plus <dbl> 332054, 356199, 1103841, 64919,…
## $ Bivalent_Booster_12Plus_Pop_Pct <dbl> 20.5, 9.1, 12.3, 13.2, 24.9, 21…
## $ Bivalent_Booster_18Plus <dbl> 319161, 349934, 1067468, 63244,…
## $ Bivalent_Booster_18Plus_Pop_Pct <dbl> 21.9, 9.8, 13.2, 14.2, 26.2, 22…
## $ Bivalent_Booster_65Plus <dbl> 151146, 184844, 476297, 34832, …
## $ Bivalent_Booster_65Plus_Pop_Pct <dbl> 48.4, 24.9, 31.4, 35.1, 55.0, 4…
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 6
## isType tot_cases tot_deaths new_cases new_deaths n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 8.64e+9 1.13e+8 1.05e+8 1131000 10207
## 2 after 8.57e+9 1.12e+8 1.03e+8 1124378 8823
## 3 pctchg 8.89e-3 4.97e-3 1.22e-2 0.00585 0.136## Warning: Using `all_of()` outside of a selecting function was deprecated in tidyselect
## 1.2.0.
## ℹ See details at
## <https://tidyselect.r-lib.org/reference/faq-selection-context.html>## Warning: Using `by = character()` to perform a cross join was deprecated in dplyr 1.1.0.
## ℹ Please use `cross_join()` instead.##
## Processed for cdcWeeklyBurden:
## Rows: 61,455
## Columns: 6
## $ date <date> 2020-01-22, 2020-01-23, 2020-01-24, 2020-01-25, 2020-01-26…
## $ state <chr> "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK",…
## $ tot_cases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tot_deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ new_cases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ new_deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 5
## isType inp hosp_adult hosp_ped n
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 before 5.84e+7 5.15e+7 1552714 63839
## 2 after 5.81e+7 5.12e+7 1523748 60849
## 3 pctchg 5.39e-3 5.14e-3 0.0187 0.0468
##
##
## Processed for cdcHosp:
## Rows: 60,849
## Columns: 5
## $ date <date> 2021-03-17, 2021-02-23, 2021-02-05, 2021-02-02, 2021-01-26…
## $ state <chr> "MA", "OR", "AR", "MA", "KY", "MD", "SD", "MA", "DC", "MT",…
## $ inp <dbl> 570, 257, 901, 1518, 1626, 2180, 189, 2098, 371, 203, 512, …
## $ hosp_adult <dbl> 555, 246, 890, 1501, 1606, 2161, 189, 2069, 343, 200, 507, …
## $ hosp_ped <dbl> 15, 11, 11, 17, 20, 19, 0, 29, 28, 3, 5, 3, 1, 3, 15, 4, 21…
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 9
## isType vxa vxc vxcpoppct vxcgte65 vxcgt…¹ vxcgte18 vxcgt…² n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 4.62e+11 1.87e+11 1661554. 4.69e+10 2.43e+6 1.72e+11 1.95e+6 3.85e+4
## 2 after 2.23e+11 9.03e+10 1389663. 2.27e+10 2.15e+6 8.33e+10 1.65e+6 3.04e+4
## 3 pctchg 5.18e- 1 5.16e- 1 0.164 5.16e- 1 1.16e-1 5.16e- 1 1.54e-1 2.09e-1
## # … with abbreviated variable names ¹vxcgte65pct, ²vxcgte18pct
##
##
## Processed for vax:
## Rows: 44,778
## Columns: 9
## $ date <date> 2020-12-14, 2020-12-15, 2020-12-16, 2020-12-17, 2020-12-1…
## $ state <chr> "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK"…
## $ vxa <dbl> 0, 0, 0, 2, 2, 1607, 4239, 5125, 5615, 6822, 8578, 10612, …
## $ vxc <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ vxcpoppct <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ vxcgte65 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ vxcgte65pct <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ vxcgte18 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ vxcgte18pct <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
##
## Integrated per capita data file:
## Rows: 62,739
## Columns: 34
## $ date <date> 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-0…
## $ state <chr> "AL", "HI", "IN", "LA", "MN", "MT", "NC", "TX", "AL", "HI"…
## $ tot_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tot_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ new_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ new_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ inp <dbl> NA, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 1877, 0, …
## $ hosp_adult <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hosp_ped <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxa <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxc <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpoppct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte65 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte65pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte18 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte18pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tdpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ cpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ dpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hpm <dbl> NA, 0.0000, 0.0000, NA, 0.0000, 0.0000, 0.0000, 0.0000, NA…
## $ ahpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ phpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxapm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tdpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ cpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ dpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ ahpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ phpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxapm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.## Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
saveToRDS(cdc_daily_230602, ovrWriteError=FALSE)The latest hospitalization data is also downloaded and processed:
# Run for latest data, save as RDS
indivHosp_20230603 <- downloadReadHospitalData(loc="./RInputFiles/Coronavirus/HHS_Hospital_20230603.csv")##
## File ./RInputFiles/Coronavirus/HHS_Hospital_20230603.csv already exists
## File will not be downloaded since ovrWrite is not TRUE## Rows: 279496 Columns: 128
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (11): hospital_pk, state, ccn, hospital_name, address, city, zip, hosp...
## dbl (114): total_beds_7_day_avg, all_adult_hospital_beds_7_day_avg, all_adu...
## lgl (2): is_metro_micro, is_corrected
## date (1): collection_week
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.## Rows: 279,496
## Columns: 128
## $ hospital_pk <chr> …
## $ collection_week <date> …
## $ state <chr> …
## $ ccn <chr> …
## $ hospital_name <chr> …
## $ address <chr> …
## $ city <chr> …
## $ zip <chr> …
## $ hospital_subtype <chr> …
## $ fips_code <chr> …
## $ is_metro_micro <lgl> …
## $ total_beds_7_day_avg <dbl> …
## $ all_adult_hospital_beds_7_day_avg <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_avg <dbl> …
## $ inpatient_beds_used_7_day_avg <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_avg <dbl> …
## $ inpatient_beds_used_covid_7_day_avg <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_avg <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_avg <dbl> …
## $ inpatient_beds_7_day_avg <dbl> …
## $ total_icu_beds_7_day_avg <dbl> …
## $ total_staffed_adult_icu_beds_7_day_avg <dbl> …
## $ icu_beds_used_7_day_avg <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_avg <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_avg <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_avg <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_avg <dbl> …
## $ icu_patients_confirmed_influenza_7_day_avg <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_avg <dbl> …
## $ total_beds_7_day_sum <dbl> …
## $ all_adult_hospital_beds_7_day_sum <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_sum <dbl> …
## $ inpatient_beds_used_7_day_sum <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_sum <dbl> …
## $ inpatient_beds_used_covid_7_day_sum <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_sum <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_sum <dbl> …
## $ inpatient_beds_7_day_sum <dbl> …
## $ total_icu_beds_7_day_sum <dbl> …
## $ total_staffed_adult_icu_beds_7_day_sum <dbl> …
## $ icu_beds_used_7_day_sum <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_sum <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_sum <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_sum <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_sum <dbl> …
## $ icu_patients_confirmed_influenza_7_day_sum <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_sum <dbl> …
## $ total_beds_7_day_coverage <dbl> …
## $ all_adult_hospital_beds_7_day_coverage <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_coverage <dbl> …
## $ inpatient_beds_used_7_day_coverage <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_coverage <dbl> …
## $ inpatient_beds_used_covid_7_day_coverage <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_coverage <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_coverage <dbl> …
## $ inpatient_beds_7_day_coverage <dbl> …
## $ total_icu_beds_7_day_coverage <dbl> …
## $ total_staffed_adult_icu_beds_7_day_coverage <dbl> …
## $ icu_beds_used_7_day_coverage <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_coverage <dbl> …
## $ icu_patients_confirmed_influenza_7_day_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_coverage <dbl> …
## $ previous_day_admission_adult_covid_confirmed_7_day_sum <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+_7_day_sum` <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_7_day_sum <dbl> …
## $ previous_day_covid_ED_visits_7_day_sum <dbl> …
## $ previous_day_admission_adult_covid_suspected_7_day_sum <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+_7_day_sum` <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_7_day_sum <dbl> …
## $ previous_day_total_ED_visits_7_day_sum <dbl> …
## $ previous_day_admission_influenza_confirmed_7_day_sum <dbl> …
## $ geocoded_hospital_address <chr> …
## $ hhs_ids <chr> …
## $ previous_day_admission_adult_covid_confirmed_7_day_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_7_day_coverage <dbl> …
## $ previous_day_admission_adult_covid_suspected_7_day_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_7_day_coverage <dbl> …
## $ previous_week_personnel_covid_vaccinated_doses_administered_7_day <dbl> …
## $ total_personnel_covid_vaccinated_doses_none_7_day <dbl> …
## $ total_personnel_covid_vaccinated_doses_one_7_day <dbl> …
## $ total_personnel_covid_vaccinated_doses_all_7_day <dbl> …
## $ previous_week_patients_covid_vaccinated_doses_one_7_day <dbl> …
## $ previous_week_patients_covid_vaccinated_doses_all_7_day <dbl> …
## $ is_corrected <lgl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_avg <dbl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_coverage <dbl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_sum <dbl> …
## $ all_pediatric_inpatient_beds_7_day_avg <dbl> …
## $ all_pediatric_inpatient_beds_7_day_coverage <dbl> …
## $ all_pediatric_inpatient_beds_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown_7_day_sum <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_avg <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_coverage <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_sum <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_avg <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_coverage <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_sum <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_avg <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_coverage <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_sum <dbl> …
##
## Hospital Subtype Counts:
## # A tibble: 4 × 2
## hospital_subtype n
## <chr> <int>
## 1 Childrens Hospitals 5072
## 2 Critical Access Hospitals 74874
## 3 Long Term 18828
## 4 Short Term 180722
##
## Records other than 50 states and DC
## # A tibble: 5 × 2
## state n
## <chr> <int>
## 1 AS 34
## 2 GU 117
## 3 MP 39
## 4 PR 2546
## 5 VI 101
##
## Record types for key metrics## Warning: Specifying the `id_cols` argument by position was deprecated in tidyr 1.3.0.
## ℹ Please explicitly name `id_cols`, like `id_cols = name`.## # A tibble: 10 × 5
## name `NA` Posit…¹ Value…² Total
## <chr> <int> <int> <int> <int>
## 1 all_adult_hospital_beds_7_day_avg 86818 192256 422 279496
## 2 all_adult_hospital_inpatient_bed_occupied_7_day… 30296 228513 20687 279496
## 3 icu_beds_used_7_day_avg 33392 215517 30587 279496
## 4 inpatient_beds_7_day_avg 8383 270071 1042 279496
## 5 inpatient_beds_used_7_day_avg 8383 248655 22458 279496
## 6 inpatient_beds_used_covid_7_day_avg 1754 184801 92941 279496
## 7 staffed_icu_adult_patients_confirmed_and_suspec… 30247 168898 80351 279496
## 8 total_adult_patients_hospitalized_confirmed_and… 27723 167879 83894 279496
## 9 total_beds_7_day_avg 62457 216790 249 279496
## 10 total_icu_beds_7_day_avg 3299 262033 14164 279496
## # … with abbreviated variable names ¹Positive, ²`Value -999999`## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
saveToRDS(indivHosp_20230603, ovrWriteError=FALSE)Post-processing is run, including hospital summaries:
# Create pivoted burden data
burdenPivotList_230602 <- postProcessCDCDaily(cdc_daily_230602,
dataThruLabel="May 2023",
keyDatesBurden=c("2023-05-10", "2022-06-30",
"2021-12-31", "2021-06-30"
),
keyDatesVaccine=c("2023-05-10", "2021-12-31",
"2021-08-31", "2021-03-31"
),
returnData=TRUE
)## Joining with `by = join_by(state)`##
## *** File has been checked for uniqueness by: state date name## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.## Warning: Removed 24 rows containing missing values (`geom_line()`).
## Warning: Removed 24 rows containing missing values (`position_stack()`).
## Warning: Removed 24 rows containing missing values (`position_stack()`).
## Warning: Removed 9 rows containing missing values (`geom_line()`).## Warning: There was 1 warning in `filter()`.
## ℹ In argument: `date %in% all_of(keyDates)`.
## Caused by warning:
## ! Using `all_of()` outside of a selecting function was deprecated in tidyselect
## 1.2.0.
## ℹ See details at
## <https://tidyselect.r-lib.org/reference/faq-selection-context.html>## Warning: Specifying the `id_cols` argument by position was deprecated in tidyr 1.3.0.
## ℹ Please explicitly name `id_cols`, like `id_cols = state`.## Warning: Specifying the `id_cols` argument by position was deprecated in tidyr 1.3.0.
## ℹ Please explicitly name `id_cols`, like `id_cols = state`.
## Warning: Specifying the `id_cols` argument by position was deprecated in tidyr 1.3.0.
## ℹ Please explicitly name `id_cols`, like `id_cols = state`.## Warning: Specifying the `id_cols` argument by position was deprecated in tidyr 1.3.0.
## ℹ Please explicitly name `id_cols`, like `id_cols = state`.
## Warning: Specifying the `id_cols` argument by position was deprecated in tidyr 1.3.0.
## ℹ Please explicitly name `id_cols`, like `id_cols = state`.## Warning: Specifying the `id_cols` argument by position was deprecated in tidyr 1.3.0.
## ℹ Please explicitly name `id_cols`, like `id_cols = state`.
# Create hospitalized per capita data
hospPerCap_230602 <- hospAgePerCapita(readFromRDS("dfStateAgeBucket2019"),
lst=burdenPivotList_230602,
popVar="pop2019",
excludeState=c(),
cumStartDate="2020-07-15"
)## Warning: Removed 18 rows containing missing values (`geom_line()`).
burdenPivotList_230602$hospAge %>%
group_by(adultPed, confSusp, age, name) %>%
summarize(value=sum(value, na.rm=TRUE), n=n(), .groups="drop")## # A tibble: 18 × 6
## adultPed confSusp age name value n
## <chr> <chr> <chr> <chr> <dbl> <int>
## 1 adult confirmed 0-19 previous_day_admission_adult_covid_con… 5.17e4 63839
## 2 adult confirmed 20-29 previous_day_admission_adult_covid_con… 3.23e5 63839
## 3 adult confirmed 30-39 previous_day_admission_adult_covid_con… 4.63e5 63839
## 4 adult confirmed 40-49 previous_day_admission_adult_covid_con… 5.44e5 63839
## 5 adult confirmed 50-59 previous_day_admission_adult_covid_con… 8.76e5 63839
## 6 adult confirmed 60-69 previous_day_admission_adult_covid_con… 1.19e6 63839
## 7 adult confirmed 70-79 previous_day_admission_adult_covid_con… 1.25e6 63839
## 8 adult confirmed 80+ previous_day_admission_adult_covid_con… 1.20e6 63839
## 9 adult suspected 0-19 previous_day_admission_adult_covid_sus… 4.62e4 63839
## 10 adult suspected 20-29 previous_day_admission_adult_covid_sus… 3.06e5 63839
## 11 adult suspected 30-39 previous_day_admission_adult_covid_sus… 4.04e5 63839
## 12 adult suspected 40-49 previous_day_admission_adult_covid_sus… 4.10e5 63839
## 13 adult suspected 50-59 previous_day_admission_adult_covid_sus… 6.52e5 63839
## 14 adult suspected 60-69 previous_day_admission_adult_covid_sus… 9.18e5 63839
## 15 adult suspected 70-79 previous_day_admission_adult_covid_sus… 9.06e5 63839
## 16 adult suspected 80+ previous_day_admission_adult_covid_sus… 8.34e5 63839
## 17 ped confirmed 0-19 previous_day_admission_pediatric_covid… 2.07e5 63839
## 18 ped suspected 0-19 previous_day_admission_pediatric_covid… 4.93e5 63839saveToRDS(burdenPivotList_230602, ovrWriteError=FALSE)
saveToRDS(hospPerCap_230602, ovrWriteError=FALSE)It appears case, death, and vaccines data may no longer be reported after May 10, 2023. This process may have reached a natural stopping point. Peaks and valleys of key metrics are also updated:
peakValleyCDCDaily(cdc_daily_230602)## Warning: Using `all_of()` outside of a selecting function was deprecated in tidyselect
## 1.2.0.
## ℹ See details at
## <https://tidyselect.r-lib.org/reference/faq-selection-context.html>## Warning: There was 1 warning in `summarize()`.
## ℹ In argument: `across(all_of(numVar), .fns = sum, na.rm = TRUE)`.
## ℹ In group 1: `date = 2020-01-01`, `regn = "North Central"`.
## Caused by warning:
## ! The `...` argument of `across()` is deprecated as of dplyr 1.1.0.
## Supply arguments directly to `.fns` through an anonymous function instead.
##
## # Previously
## across(a:b, mean, na.rm = TRUE)
##
## # Now
## across(a:b, \(x) mean(x, na.rm = TRUE))## Warning: Removed 6 rows containing missing values (`geom_line()`).
## Warning: Removed 6 rows containing missing values (`geom_line()`).
## Warning: Removed 6 rows containing missing values (`geom_line()`).
## Warning: Removed 20 rows containing missing values (`geom_line()`).
## Warning: Removed 20 rows containing missing values (`geom_line()`).
## # A tibble: 10,956 × 8
## date state vxa vxc vxa_isPeak vxc_isPeak vxa_isValley vxc_isValley
## <date> <chr> <dbl> <dbl> <lgl> <lgl> <lgl> <lgl>
## 1 2020-12-01 CA NA NA FALSE FALSE FALSE FALSE
## 2 2020-12-01 FL NA NA FALSE FALSE FALSE FALSE
## 3 2020-12-01 GA NA NA FALSE FALSE FALSE FALSE
## 4 2020-12-01 IL NA NA FALSE FALSE FALSE FALSE
## 5 2020-12-01 MI NA NA FALSE FALSE FALSE FALSE
## 6 2020-12-01 NC NA NA FALSE FALSE FALSE FALSE
## 7 2020-12-01 NJ NA NA FALSE FALSE FALSE FALSE
## 8 2020-12-01 NY NA NA FALSE FALSE FALSE FALSE
## 9 2020-12-01 OH NA NA FALSE FALSE FALSE FALSE
## 10 2020-12-01 PA NA NA FALSE FALSE FALSE FALSE
## # … with 10,946 more rowsHospital data are pieced together as needed:
# Create modified hospital data
multiSourceHosp_20230602 <- multiSourceDataCombine(list(readFromRDS("indivHosp_20220704"),
readFromRDS("indivHosp_20230603")
),
timeVec=as.Date("2022-01-01")
)